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1 Islam, Md. M.; Sarker, Md. A.; Mamun, Md. A. A.; Mamun-ur-Rashid, Md.; Roy, D. 2021. Stepping up versus stepping out: on the outcomes and drivers of two alternative climate change adaptation strategies of smallholders. World Development, 148:105671. (Online first) [doi: https://doi.org/10.1016/j.worlddev.2021.105671]
Climate change adaptation ; Strategies ; Smallholders ; Farmers ; Farmland ; Households ; Livelihoods ; Food security ; Vulnerability ; Indicators ; Villages / Bangladesh
(Location: IWMI HQ Call no: e-copy only Record No: H050683)
(2.32 MB)
Which of the two climate change adaptation strategies – adjusting or improving farming (defined as Stepping Up) versus reducing or exiting farming (defined as Stepping Out) – provides better developmental outcomes for smallholder farmers? Are the drivers of these two strategies different? Do the outcomes and drivers vary according to farmland holding size? We investigated these unanswered questions, inspired primarily by a widespread but unverified suggestion that stepping out of farming can be a better option for smallholders. We utilised recent survey data from over eight hundred smallholder households located in climatic hazard-prone areas in Bangladesh. We applied a holistic Driver-Strategy-Outcome analytical framework and rigorous statistical methods, including index-based data aggregation, and Structural Equation Modelling with ‘mediation’ and ‘moderation’ analyses. Contrary to widespread speculations, we found that Stepping Out had a large negative effect on smallholders’ livelihood Outcomes; while Stepping Up had a moderate, but positive effect. The natural-environmental Drivers of Stepping Up and Stepping Out were similar; however, the psychological-institutional Drivers of each differed, with the same factor acting as a driver for one strategy whilst as a deterrent for the other. We found significant ‘mediatory’ effects of both the adaptation Strategies on Outcomes as well as significant ‘moderation’ effects of farmland holding size on the Drivers and Outcomes, with the positive effect of Stepping Up observed for smallholders owing lands of <2.5 acres only. We call for relevant policies and interventions to exercise caution in promoting smallholders’ exit from agriculture, and to adopt appropriate mitigating measures to manage such a transition. Moreover, smallholder agricultural development initiatives should not discount even the ‘smallest of smallholders’ and support them through ‘diverse and complementary innovations’ as well as ‘tailored’ institutional support services, especially for those living in proximity to hazard hotspots.
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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