COVER FEATURE
Machine Learning and Global Climate Modelling for Projecting Irrigation for Rice Cultivation
By Muhammad Adib Mohd Nasir Department of Biological and Agricultural Engineering Faculty of Engineering , Universiti Putra Malaysia
COVER FEATURE
Did you know that the irrigation supply for large-scale rice granaries comes from river basins and rivers ? Malaysia has rice granaries that contribute up to 690,000 ha relying on these water resources ( Dorairaj & Govender , 2023 ). Massive amounts of water are required to irrigate the rice granaries , which have become a pillar of sustainable rice production in Malaysia . However , the use of water resources requires proper management and planning to ensure their availability to fulfil irrigation demand . Therefore , future prediction of the availability of water resources is crucial to strategise rice granary cultivation .
It is not an easy task to deal with hydrological system stochasticity which is at the same time worsened by climate change . Today , machine learning ( ML ), a branch of artificial intelligence ( AI ), has shown its talent as a powerful predictive tool in agro-hydrological modelling . In addition , with the huge amount of data required for Global Climate Models ( GCMs ), ML is becoming crucial in handling this data with impressive accuracy . The goal of ML and GCMs in agro-hydrology is to improve prediction outcomes , reduce the cost and time of implementation , and advance irrigation and water resources research .
Rice Granary
Rice is cultivated across 142 million hectares globally and serves as a staple food for the world .
Figure 1 : The eight rice granary areas in Peninsular Malaysia
Approximately 90 % of it is found in the monsoon regions of Asia , with 75 % relying on irrigation supply while the rest is rainfed ( Rowshon et al ., 2019 ). In Malaysia , two-thirds of rice granaries are distributed across Peninsular Malaysia , and the remainder is in Sabah and Sarawak . It encompasses eight main rice granaries located in Peninsular Malaysia as shown in Figure 1 : Muda Agricultural Development Authority ( MADA ),
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