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1 Kaur, H.; Dhillon, S. S.; Mander, G.; Bath, K. S. 2000. Occurrence of heavy metals in water and sediment of the River Satluj in Punjab. In Trivedy, R. K. (Ed.), Pollution and biomonitoring of Indian Rivers. Jaipur, India: ABD Publishers. pp.176-180.

Rivers ; Sedimentary materials ; Water pollution ; Water quality ; Effluents / India / Punjab / River Satluj

(Location: IWMI-HQ Call no: 574.526323 G635 TRI Record No: H028424)

2 Thapa, R.; Gupta, S.; Guin, S.; Kaur, H.. 2018. Sensitivity analysis and mapping the potential groundwater vulnerability zones in Birbhum District, India: a comparative approach between vulnerability models. Water Science, 32(1):44-66. [doi: https://doi.org/10.1016/j.wsj.2018.02.003]

Groundwater assessment ; Sensitivity analysis ; Mapping ; Models ; Forecasting ; Groundwater recharge ; Aquifers ; Hydraulic conductivity ; Soil types ; Land use ; Land cover / India / West Bengal / Birbhum

(Location: IWMI HQ Call no: e-copy only Record No: H048836)

https://www.sciencedirect.com/science/article/pii/S1110492917300085/pdfft?md5=51b266dc01392ceeef29146aaa27a3d4&pid=1-s2.0-S1110492917300085-main.pdf
https://vlibrary.iwmi.org/pdf/H048836.pdf

(10.80 MB) (10.8 MB)

The assessment of groundwater vulnerability is essential especially in developing areas, where agriculture is the main source of the population. In the present study, four different overlay and index method, namely, DRASTIC, modified DRASTIC, pesticide DRASTIC and modified pesticide DRASTIC are implemented with a view to identifying the most appropriate method that predicts the vulnerable zone to groundwater pollution. Sensitivity analysis reveals that net recharge is the most influential parameter of the vulnerability index. Cross comparison of model output shows the highest similarity of 97% is observed between drastic and modified drastic while the maximum difference in models prediction of 49% is observed between modified drastic and pesticide drastic. Reported nitrate concentrations in groundwater are considered for validation of model-generated final output map. The prediction power of the models are assessed using success and prediction rate method and it highlights DRASTIC model as the most suitable model with 89.69% and 84.54% of the area under area under the curve (AUC) for success and prediction rate respectively.

3 Kaur, H.; Srinivas, A.; Bazaz, A. 2021. Understanding access to agrarian knowledge systems: perspectives from rural Karnataka. Climate Services, 21:100205. [doi: https://doi.org/10.1016/j.cliser.2020.100205]

Agricultural extension ; Climate change adaptation ; Risk ; Knowledge ; Access to information ; Information dissemination ; Farmers ; Communities ; Collective action ; Villages ; Households ; Socioeconomic environment ; Institutions / India / Karnataka / Gulbarga / Kolar / Bangalore

(Location: IWMI HQ Call no: e-copy only Record No: H050347)

https://www.sciencedirect.com/science/article/pii/S2405880720300571/pdfft?md5=79ff98d10d962a052a77e44f14bcc9bb&pid=1-s2.0-S2405880720300571-main.pdf
https://vlibrary.iwmi.org/pdf/H050347.pdf

(3.21 MB) (3.21 MB)

In this paper, we attempt to unpack the existing landscape of agricultural extension services and delve into questions of access to and localisation of knowledge to understand how these conditions (access and localisation) determine climate change adaptation in agriculture in the southern Indian state of Karnataka. Our empirical findings suggest that the current extension framework reproduces existing inequalities in that access to institutional knowledge and its uptake is linked to one’s social location, that is, caste, gender, class, and geographic location, and information shared is neither timely nor contextually relevant. Employing accessibility and localization as lenses of inquiry, we argue from empirical evidence that smallholder farmers in a rain-fed context are especially vulnerable to the risks posed by climatic change and hence agricultural extension (with climate-informed knowledge) should be to be seen as a critical enabler of adaptation; ensuring accessibility and localisation, we argue, strengthens climate services, and by extension, enables adaptation to climatic risks. The issues that encumber effective extension, we contend, can be mitigated by a re-imagination of agricultural extension, one that privileges public field level functionaries as conduits between state departments and farmers over other modes, and enables structured involvement of community collectives as vehicles to address local needs and ensure access. Drawing on interventions in our study sites, we make a case for promoting knowledge systems that ensure access to climate-specific agricultural information and contextual embeddedness.