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Mali, Santosh S.
- Assessing Water Footprints and Virtual Water Flows in Gomti River Basin of India
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PDF Views:69
Authors
Affiliations
1 ICAR-Research Complex for Eastern Region, Research Centre, Ranchi 834 010, IN
2 Water Technology Centre, Indian Agricultural Research Institute, New Delhi 110 012, IN
1 ICAR-Research Complex for Eastern Region, Research Centre, Ranchi 834 010, IN
2 Water Technology Centre, Indian Agricultural Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 115, No 4 (2018), Pagination: 721-728Abstract
This article analyses the blue, green and grey water footprints and virtual water flows within the Gomti river basin (GRB) in India. Assessments were made at spatial resolution of agricultural production units (APUs). An APU is a homogeneous spatial unit delineated on the basis of soil type, agro-ecological region and district boundaries. Water footprints of crop production and consumption were compared to arrive at virtual water balance within the GRB. Results show that water footprint of GRB was 12,773 million m3 year–1. Crop production was the largest water consumer accounting for 95.5% of water footprint within the basin. The higher proportion of blue water footprint (47.3%) indicates the dependence of GRB on irrigated agriculture. Contribution of rainfed agriculture to total water footprint was about 11.2%. Considerable portion of blue water is used in the production of low value water-intensive crops. The GRB was assessed as a net virtual water importer, indicating its dependence on the water resources of other river basins; it imports 2945 million m3 virtual water annually. This scenario can be changed if the area allocated to different water-intensive crops is optimized and limited to the extent that meets the consumption needs within the basin, leading to reduction in production surplus of these crops.Keywords
Economic Water Productivity, River Basin, Virtual Water Flow, Water Footprint.Full Text
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- Predicting the Invasion Potential of Indigenous Restricted Mango Fruit Borer, Citripestis Eutraphera (Lepidoptera:Pyralidae) in India Based on Maxent Modelling
Abstract Views :232 |
PDF Views:84
Authors
Affiliations
1 ICAR Research Complex for Eastern Region, Research Centre, Plandu, Ranchi 834 010, IN
2 Division of Crop Protection, ICAR-Central Institute for Cotton Research, Nagpur 440 010, IN
1 ICAR Research Complex for Eastern Region, Research Centre, Plandu, Ranchi 834 010, IN
2 Division of Crop Protection, ICAR-Central Institute for Cotton Research, Nagpur 440 010, IN
Source
Current Science, Vol 116, No 4 (2019), Pagination: 636-642Abstract
The mango fruit borer, Citripestis eutraphera (Meyrick), originally confined to the Andaman Islands, is a recent invasion in mainland India. With changes in climatic conditions, the pest is likely to spread in other major mango-growing regions of the country and can pose serious threats to mango production. In this backdrop, the present study examines the impact of climate change to develop spatio-temporal distribution of invasive C. eutraphera in India using the maximum entropy (MaxEnt) modelling approach. Integration of point data on current occurrence of pest and corresponding bioclimatic variables in MaxEnt were used to define the potential distribution in India and mapped using spatial analysis tool in ArcGIS. The model framework performed well as indicated by high area under the curve (0.97) value. Jackknife test for estimating predictive power of the variables indicated that ‘isothermality’ and ‘temperature seasonality’ significantly affected C. eutraphera distribution. It was found that mango-growing pockets in the southwestern parts of Gujarat, as well as parts of Kerala and Tamil Nadu were moderately to highly suitable for C. eutraphera distribution in 2050 and 2070. The results of this study could be an important guide for selecting monitoring and surveillance sites and designing integrated pest management policies in the context of climate change against this invasive pest of mango.Keywords
Climate Change, Mango, Invasive Pest, Species Distribution Models.Full Text
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