The use of semi-structured interviews for the characterisation of farmer irrigation practices

Authors

Jimmy O’Keeffe, Imperial College LondonFollow
Wouter Buytaert, Imperial College London
Ana Mijic, Imperial College London
N. Brozovic, University of Nebraska-LincolnFollow
Rajiv Sinha, Indian Institute of Technology Kanpur

Date of this Version

5-12-2016

Document Type

Article

Citation

Author(s) 2016.

Comments

Hydrol. Earth Syst. Sci., 20, 1911–1924, 2016 www.hydrol-earth-syst-sci.net/20/1911/2016/ doi:10.5194/hess-20-1911-2016

Abstract

For the development of sustainable and realistic water security, generating information on the behaviours, characteristics, and drivers of users, as well as on the resource itself, is essential. In this paper we present a methodology for collecting qualitative and quantitative data on water use practices through semi-structured interviews. This approach facilitates the collection of detailed information on actors’ decisions in a convenient and cost-effective manner. Semi-structured interviews are organised around a topic guide, which helps lead the conversation in a standardised way while allowing sufficient opportunity for relevant issues to emerge. In addition, they can be used to obtain certain types of quantitative data. While not as accurate as direct measurements, they can provide useful information on local practices and users’ insights. We present an application of the methodology on farmer water use in two districts in the state of Uttar Pradesh in northern India. By means of 100 farmer interviews, information was collected on various aspects of irrigation practices, including irrigation water volumes, irrigation cost, water source, and their spatial variability. Statistical analyses of the information, along with data visualisation, are also presented, indicating a significant variation in irrigation practices both within and between districts. Our application shows that semi-structured interviews are an effective and efficient method of collecting both qualitative and quantitative information for the assessment of drivers, behaviours, and their outcomes in a data-scarce region. The collection of this type of data could significantly improve insights on water resources, leading to more realistic management options and increased water security in the future.