Your search found 3 records
1 Alauddin, M.; Sharma, Bharat R. 2013. Inter-district rice water productivity differences in Bangladesh: an empirical exploration and implications. Ecological Economics, 93:210-218. [doi: https://doi.org/10.1016/j.ecolecon.2013.05.015]
Water productivity ; Crops ; Rice ; Intensification ; Indicators ; Technology ; Groundwater irrigation ; Irrigated sites ; Land productivity ; Drought ; Salinity ; Policy ; Factor analysis / Bangladesh
(Location: IWMI HQ Call no: e-copy only Record No: H045904)
(1.04 MB)
While the bulk of research on crop water productivity (WP) focuses on static cross-section analysis, this research provides a spatio-temporal perspective. It estimates rice crop WP for 21 Bangladesh districts for 37 years; exploresWP variations among districts; and investigates causality involving WP, intensification and technological variables; and groundwater irrigation and depth. It breaks new grounds by probing these significant but unexplored issues.Technological diffusion was the key factor explaining inter-district WP differences. The impact of agricultural intensification on rabi (dry season) and kharif (wet season) crop WPs was positive and negative respectively. Dummy variables typifying policy transition negatively impacted on WPs for both kharif and overall crops. While rabi and kharif rice WPs grew with time, overall crop WP recorded the strongest growth. Rabi and overall WPs were lower in salinity- and drought-prone districts covering 33% of Bangladesh's net cropped area (NCA). In 90% of Bangladesh's NCA districts, technological diffusion caused WP. Causality existed between groundwater irrigation and depth in 60% NCA. Despite significant potential to increaseWP, increasing dependence on groundwater appears unsustainable. Widespread diffusion of HYVs in the kharif season, and development of salinity and drought-tolerant rice varieties could go a long way in sustaining rice WP.
2 Nazir, H. M.; Hussain, I.; Zafar, M. I.; Ali, Z.; AbdEl-Salam, N. M. 2016. Classification of drinking water quality index and identification of significant factors. Water Resources Management, 30(12):4233-4246. [doi: https://doi.org/10.1007/s11269-016-1417-4]
Drinking water ; Water quality ; Water pollution ; Chemicophysical properties ; Factor analysis ; Pipes ; Cluster sampling / Pakistan / South Punjab / Mianwali / Khushab / Layyah / Bhakkar / Dera Ghazi Khan / Muzaffargarh / Rajanpur / Rahim Yar Khan
(Location: IWMI HQ Call no: e-copy only Record No: H047692)
(0.57 MB)
Water pipes are considered to be one of responsible sources for the water pollution. Among these sources of water supply, the water pipes are the only source of carrying out fresh or processed water into lakes, ponds and streams etc. In Pakistan, knowledge on the condition of water pipes is scarce as deterioration of water pipes are hardly inspected due to high cost. The aim of the current research was to examine the quality of water pipelines of eight districts of South-Punjab, namely, Mianwali, Khushab, Layyah, Bhakkar, Dera Ghazi Khan, Muzaffargarh, Rajanpur and Rahim Yar Khan. Selected sampling stations were analyzed for physio-chemical parameters such as pH, Total Dissolve Solids (TDS), Sulfate (SO4), Chlorine (Cl), Calcium (Ca), Magnesium (Mg), Hardness, Nitrate (NO3), Fluoride (F) and Iron (Fe). The data pertaining water monitoring contain different parameters and seem difficult work for the interpretation of water quality by managing different parameters separately. For this purpose, National Sanitation Foundation Water Quality Index (NSF-WQI) was determined to communicate the quality of water in a simple form. Besides this, groups comprising of similar sampling sites based on water quality characteristics were identified using unsupervised technique. Factor Analysis (FA) has been performed for extracting the latent pollution sources that may cause the more variance in large and complex data. The calculated values of WQI from 1600 sampling stations ranging from 20.73 to 223.74 are divided into five groups; Excellent to Unsuitable class of waters with the average value 62.09 described as good limit for drinking water. Further sampling stations are divided into five optimal clusters selected with suitable k value obtained from Silhouette coefficient. Results of k-means clustering are also verified with natural groups made by WQI. Analysis of multivariate techniques showed several factors to be responsible for the water quality deterioration. It is found out from the FA that three latent factors such as organic pollution, agriculture run-off and urban land use caused 83.30 % of the total variation. Hence, water quality management and control of these latent factors are strongly recommended.
3 Raahalya, S.; Balasubramaniam, P.; Devi, M. N.; Maragatham, N.; Selvi, R. G. 2024. Farmers' resilience index: a tool to metricize the resilience of the farmers towards natural disasters affecting agriculture in India. Water Policy, 26(1):79-93. [doi: https://doi.org/10.2166/wp.2023.152]
Natural disasters ; Agriculture ; Farmers ; Resilience ; Factor analysis ; Principal component analysis ; Models ; Cyclones ; Livelihoods ; Indicators ; Human capital ; Social capital ; Natural capital / India / Andhra Pradesh / Krishna Godavari Basin / East Godavari District / West Godavari District / Krishna District / Guntur District
(Location: IWMI HQ Call no: e-copy only Record No: H052601)
https://iwaponline.com/wp/article-pdf/26/1/79/1358363/026010079.pdf
https://vlibrary.iwmi.org/pdf/H052601.pdf
https://vlibrary.iwmi.org/pdf/H052601.pdf
(0.72 MB) (740 KB)
In the present paper farmers' resilience index (FRI) was constructed considering the natural disaster using five dimensions including physical, social, economic, human and natural. The scale is administered to the 240 paddy farmers in two coastal districts of Andhra Pradesh. Principal component analysis was performed in order to fix the weightage for each variable. About (39.58%) of farmers are resilient to natural disasters with the highest resilience score for financial capital (0.641) and natural capital with less resilience score (0.401). Confirmatory factor analysis (CFA) was performed to determine how well the generated model of the scale fits the data. The structural equation modelling (SEM) path diagram was developed based on the conceptual model, which uses resilience as a latent variable. The SEM analysis revealed that four dimensions of capital positively affect farmers' resilience except for the human capital which negatively affects resilience. To reduce the effects of natural catastrophes in the upcoming years, the adaptation strategies from the highly resilient places can be examined and put into practice in the less resilient areas. It is imperative that development programmes at all levels incorporate climate awareness and stakeholder capacity building.
Powered by DB/Text WebPublisher, from
