A machine learning approach to localizing methane emission factors in rice cultivation : (Record no. 833)

MARC details
000 -LEADER
fixed length control field 03753nas a2200421 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260817161321.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 251105s20259999th u ms t 000 eng d
035 ## - SYSTEM CONTROL NUMBER
System control number .b12467583
099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number AIT Thesis no.EV-25-21
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Thet Htar Nyo
245 10 - TITLE STATEMENT
Title A machine learning approach to localizing methane emission factors in rice cultivation :
Remainder of title a case study in Ayutthaya, Thailand
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Pathum Thani, Thailand :
Name of publisher, distributor, etc. Asian Institute of Technology,
Date of publication, distribution, etc. 2025
300 ## - PHYSICAL DESCRIPTION
Extent 95 leaves :
Other physical details ill.+
Accompanying material 1 online resource
490 1# - SERIES STATEMENT
Series statement Thesis ;
Volume/sequential designation no. EV-25-21
500 ## - GENERAL NOTE
General note A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Environmental Engineering and Management
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M. Eng.) - Asian Institute of Technology, 2025
520 ## - SUMMARY, ETC.
Summary, etc. Methane (CH₄) emissions from rice cultivation significantly contribute to agricultural greenhouse gas emissions. Conventional estimation practices in developing countries often rely on generalized emission factors recommended by the Intergovernmental Panel on Climate Change (IPCC), due to the lack of sufficient ground-based monitoring and measurement. These estimations may not reflect the specific agricultural practices and environmental conditions of local regions. This study aims to localize methane emission factors for rice cultivation in Ayutthaya, Thailand, using a machine learning-based approach. A Gradient Boosting regression model was developed and trained on a global literature dataset containing known CH₄ emissions and associated agricultural variables. Key features influencing CH₄ emissions; pH, water regime, crop duration, organic amendment, N amount, and soil properties were selected based on feature importance analysis and SHAP (SHapley Additive exPlanations) values. The trained model was then applied to a Thailand-specific secondary dataset to predict methane emission factors. Results show that the predicted average CH₄ emission factor for the Thailand dataset is 0.8518 kg CH₄/ha/day, which is significantly lower than the IPCC Tier 1 default value of 1.30 kg CH₄/ha/day. This suggests that the IPCC default value may substantially overestimate methane emissions in certain local contexts, highlighting the importance of developing localized emission factors for improved regional GHG assessments. This research provides a practical framework for applying machine learning to improve emission factor localization. The findings offer valuable insights for policymakers and researchers in developing tailored mitigation strategies for rice agriculture in Southeast Asia.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Rice
General subdivision Planting
-- Environmental aspects
Geographic subdivision Thailand
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Rice
General subdivision Planting
-- Data processing
Geographic subdivision Thailand
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Xue, Wenchao,
Relator term Chairperson
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Cruz, Simon Guerrero,
Relator term Co-chairperson
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Ekbordin Winijkul,
Relator term Examination Committee
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tsusaka, Takuji W.,
Relator term Examination Committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element AIT Scholarship,
Relator term Scholarship Donor
810 2# - SERIES ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Asian Institute of Technology.
Title of a work Thesis ;
Volume/sequential designation no. EV-25-21
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Full-Text
Uniform Resource Identifier <a href="http://203.159.5.9/ait-thesis/detail.php?q=B23006">http://203.159.5.9/ait-thesis/detail.php?q=B23006</a>
907 ## - LOCAL DATA ELEMENT G, LDG (RLIN)
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b mnait
c a
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a 260617
998 ## - LOCAL CONTROL INFORMATION (RLIN)
Operator's initials, OID (RLIN) 0
Cataloger's initials, CIN (RLIN) 251110
First date, FD (RLIN) m
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945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnarc
945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l mnarc
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 67-Electronic Resource
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type 40-Archives
909 ## - LOCAL ITEMS USED
Barcode Barcode : -
CREATED CREATED : 2025-05-11
RECORD Id RECORD # : i13560943
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Barcode Barcode : 30050120420814
CREATED CREATED : 2026-06-17
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Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Total checkouts Full call number Date last seen Copy number Price effective from Koha item type Barcode
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library Archives 17/08/2026   AIT Thesis no.EV-25-21 17/08/2026 1 17/08/2026 67-Electronic Resource  
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library Archives 17/08/2026   AIT Thesis no.EV-25-21 17/08/2026 1 17/08/2026 40-Archives 30050120420814
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