Research work·
Viet Nam·2025 [Tên tác giả] — [Đơn vị công tác]
[Tên tạp chí hoặc hội nghị]
This study develops a machine learning model to forecast rice yield based on climate, soil and cropping history data from several provinces in the Mekong Delta. The model achieves higher forecast accuracy than traditional statistical methods. Findings suggest potential for wider application in other rice-growing areas across CLMV.
Smart agricultureDOI 10.48550/clmv.2026.002Machine translation
International article·
Myanmar·2025 [Tên tác giả] — [Đơn vị công tác]
[Tên tạp chí hoặc hội nghị]
This article evaluates the use of low-cost soil moisture sensor networks to guide irrigation scheduling in Myanmar's central dry zone. Field trial data indicate meaningful water savings without yield loss when sensor-guided scheduling replaces fixed calendar irrigation. The article discusses scaling considerations for similar dry zone contexts.
Smart agricultureDOI 10.48550/clmv.2026.010Original
International article·
Cambodia·2025 [Tên tác giả] — [Đơn vị công tác]
[Tên tạp chí hoặc hội nghị]
This article examines the drought resilience of smallholder rice farming systems around the Tonle Sap floodplain, drawing on household survey data collected over three growing seasons. Findings indicate that access to supplementary irrigation is the strongest predictor of yield stability. The article discusses implications for regional agricultural resilience programmes.
Smart agricultureDOI 10.48550/clmv.2026.004Original