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1 Uddin, Md. T.; Dhar, A. R. 2020. Assessing the impact of water-saving technologies on boro rice farming in Bangladesh: economic and environmental perspective. Irrigation Science, 38(2):199-212. [doi: https://doi.org/10.1007/s00271-019-00662-2]
Water conservation ; Technology ; Agricultural productivity ; System of Rice Intensification ; Farmers attitudes ; Farm income ; Water productivity ; Water use ; Water requirements ; Crop yield ; Environmental impact / Bangladesh / Mymensingh / Comilla / Bogra / Gaibandha
(Location: IWMI HQ Call no: e-copy only Record No: H049563)
(0.72 MB)
The study was conducted to evaluate the economic and environmental impacts of water-saving technologies (WST) on Boro rice (Oryza sativa; var. BRRIdhan 29) farming in Bangladesh. A total of 480 farmers (80 focal and 400 control) were selected as sample from Mymensingh, Comilla, Bogra and Gaibandha districts. Focal farmers were selected purposively and a limited amount of financial support was provided to them to implement WST. On the other hand, control farmers were selected randomly. They did not receive any financial support and continued practicing conventional irrigation methods. For analyzing the data, a combination of descriptive, mathematical and statistical techniques was used. The study revealed that 62.5 and 37.5% of focal farmers adopted alternate wetting and drying (AWD) and system of rice intensification (SRI) methods, respectively, where the majority of them were within the late majority group in terms of adoption. The profitability and productivity of Boro rice, as well as water productivity, were comparatively higher for focal farmers compared to control farmers. Furthermore, focal farmers’ irrigation amount for producing Boro rice was significantly lower than control farmers. The study also revealed that focal farmers’ income from rice production was 24.6% higher than control farmers. Input support, motivation, training programs and extension services are recommended to implement to raise the awareness and enrich the knowledge of the farmers on water-saving technologies.
2 Mia, Md. U.; Rahman, M.; Elbeltagi, A.; Abdullah-Al-Mahbub, Md.; Sharma, G.; Islam, H. M. T.; Pal, S. C.; Costache, R.; Towfiqul Islam, A. R. Md.; Islam, Md. M.; Chen, N.; Alam, E.; Washakh, R. M. A. 2022. Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology. Geocarto International, 30p. (Online first) [doi: https://doi.org/10.1080/10106049.2022.2112982]
Flooding ; Risk assessment ; Disaster risk management ; Machine learning ; Blockchain technology ; Neural networks ; Sustainable development ; Floodplains ; Rain ; Forecasting ; Datasets ; Mapping ; Normalized difference vegetation index ; Models / Bangladesh / Brahmaputra River / Jamalpur / Gaibandha / Kurigram / Bogra
(Location: IWMI HQ Call no: e-copy only Record No: H051339)
https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2112982
https://vlibrary.iwmi.org/pdf/H051339.pdf
https://vlibrary.iwmi.org/pdf/H051339.pdf
(5.41 MB) (5.41 MB)
The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.
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