000			03669nam a22003017a 4500
008			260608t2025 bx ab||g |||| 00| 0 eng d
040			_aUniversiti Teknologi Brunei _beng _cUTB
084			_aUTB 120 REPORT THESIS & DISSERTATION _aRTDS 408
100	1		_aNor Hamizah binti Haji Mohd Rhymee _eauthor.
245	1	0	_aClimate change impact on irrigation water requirement and crop yield for paddy in Brunei Darussalam / _cNor Hamizah binti Haji Mohd Rhymee
260			_aBandar Seri Begawan : _bUniversiti Teknologi Brunei, _c2025.
300			_a154 pages : _billustrations, maps ; _c30 cm.
500			_aThesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy.
500			_aABSTRACT Climate change poses unprecedented challenges to agricultural systems, particularly for small nations reliant on rice imports like Brunei Darussalam. With the vast majority of rice consumed being imported and climate change expected to significantly alter rainfall patterns and increase temperatures across Brunei, developing climate-resilient rice production strategies has become critically urgent. This study provides the first integrated assessment of climate change impacts on rice yields and irrigation water requirement (IR) in Brunei by combining statistical downscaling of Coupled Model Intercomparison Project 6 (CMIP6) scenarios, normalised vegetation difference index (NDVI)-based machine learning yield forecasting, and Water Evaluation And Planning (WEAP) software; a novel methodological framework for tropical rice systems. Future climate scenarios were assessed using statistical downscaling (SD) of multi-model ensemble (MME) techniques to simulate precipitation, minimum and maximum temperatures under different Shared Socioeconomic Pathways (SSPs) outlined by the CMIP6. The downscaled climate models were then used to forecast peak NDVI using linear regression and random forest (RF) regression, with RF showing better performance. The developed peak-NDVI models predicted paddy yield for both main and off seasons for three future periods within 2020-2100. Key findings reveal distinct seasonal and temporal patterns: the main season's NDVI showed consistent trends across SSPs, with paddy yields declining slightly in the near future, increasing mid-future, and dropping sharply in the far future (30-40% reduction under SSP585), while off-season yields exhibited significant near-term trends alongside higher IWR due to hotter, drier conditions. Seasonal rainfall shifts disrupted current planting calendars, necessitating adaptation strategies. Shifting the main season to October improved irrigation water use efficiency (IUE) by 31-33%, while delaying the off-season to June enhanced IWE by 40-46. By integrating modern technologies (NDVI, machine learning) to overcome traditional challenges, this study bridges climate projections with on-farm decisions, offering actionable strategies and laying the groundwork for future agricultural and climatology advancements in Brunei.
502			_aDissertation (Doctor of Philosophy) - Universiti Teknologi Brunei (2025)
504			_aIncludes references.
610		4	_aUniversiti Teknologi Brunei _vThesis
610		4	_aUniversiti Teknologi Brunei _vFinal Year Report
650		4	_aDissertation, Academic
650		4	_aThesis writing
650		4	_adissertation Universiti Teknologi Brunei.
650		4	_aCivil Engineering
710			_aUniversiti Teknologi Brunei
942			_2lc _cRTDS
998			_eReport, Thesis & Dissertation _s850580 : 002452 c.1 UTB _xUniversiti Teknologi Brunei
999			_c24216 _d24216