Management and agricultural water productivity improvement in India and the Ganges Basin
2025
Sharma, Bharat | Sikka, Alok | Lautze, Jonathan F. | Mabhaudhi, T.
The fast transition of India from a water-stressed to a water-scarce country has provided additional impetus for the search for interventions and decision support systems (DSSs) for solutions to problems arising from a mismatch between demand and supply and competing demands of economic and environmental sectors. Agriculture is the largest user of freshwater and increasing water productivity in agriculture is a national challenge requiring urgent attention. Globally, DSSs have gained immense popularity in various domains and, more recently, in agriculture and water resources management but are still limited in developing countries like India. This chapter presents a comprehensive compilation and stocktake of the DSSs developed at various scales in India and specific to the Ganges Basin, aimed at serving as a foundation for future work in this direction. After the criteria-based screening of the literature and reports, each selected DSS was analysed in the context of the aspects covered, key criteria, and the parameters such as relevance, applicability, focus, and scale of application. To reinforce the recommendations, KIIs were held with selected experts and stakeholders. A matrix approach was employed to compile and review the DSS with broader segregation under (i) crop and farm-based decision support systems, (ii) DSS based on artificial intelligence, enhanced machine learning, fuzzy multi-criteria decision making, and knowledge systems, (iii) DSS for real-time operation of micro-irrigation systems, (iv) DSS for management of tanks and reservoirs for water-deficit regions, and (v) DSS for improving water productivity under canal commands and conjunctive management of surface and groundwater resources. The selected DSSs were then analysed for focus, key findings, relevance, applicability, and scale of application of the DSS.
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Эту запись предоставил International Livestock Research Institute