Seasonal variation and its impacts in rice-growing regions of the Mekong Delta

Hong, Nguyen Van; Yen, Hoang Phan Hải

doi:10.1108/ijccsm-05-2020-0048

Cited by 4 publications

(3 citation statements)

References 21 publications

(22 reference statements)

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“…The main seasons in the Mekong Delta are dry and rainy. The dry season typically occurs from December to May, whereas the rainy season occurs from June to November (V. H. Nguyen & Yen, 2021). The dry season is critical for rice production, as more than 80% of rice is planted during this time when water flow is at its lowest (Thanh Nguyen Giao & Thi Hong Huynh Nhien, n.d.).…”

Section: Figure 1 the Geographical Location And Metrological Stations...mentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duc

2024

Preprint

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Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, particularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought prediction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to conventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

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Section: Figure 1 the Geographical Location And Metrological Stations...mentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duc

2024

Preprint

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“…The main seasons in the Mekong Delta are dry and rainy. The dry season typically occurs from December to May, whereas the rainy season occurs from June to November [42]. The dry season is critical for rice production, as more than 80% of rice is planted during this time when water flow is at its lowest [43].…”

Section: Study Areamentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duong,

Phong,

et al. 2024

Preprint

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Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, par-ticularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought pre-diction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to con-ventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

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“…At present, Vietnam's agriculture industry is dealing with a decrease in arable area, increased attacks of pests and droughts and negative effects on farming. These are mostly the result of climate change (Yen, 2021), as rising temperatures reduce water resources and affect crop and animal outputs. It also turns cultivable areas into barren lands due to water shortage.…”

Section: Introductionmentioning

confidence: 99%

Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam

Chandio,

Zhang,

Akram

et al. 2024

IJCCSM

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Purpose This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018. Design/methodology/approach Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis. Findings The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam. Research limitations/implications This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production. Originality/value To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

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Seasonal variation and its impacts in rice-growing regions of the Mekong Delta

Cited by 4 publications

References 21 publications

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam

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