title: Are small-scale irrigators water use efficient? Evidence from lake Naivasha basin, Kenya.
abstract: With increasing water scarcity and competing uses and users, water use efficiency is becoming increasingly important in many parts of developing countries. The lake Naivasha basin has an array of different water users and uses ranging from large scale export market agriculture, urban domestic water users to small holder farmers. The small scale farmers are located in the upper catchment areas and form the bulk of the users in terms of area and population. This study used farm household data to explore the overall technical efficiency, irrigation water use efficiency and establish the factors influencing water use efficiency among small scale farmers in the Lake Naivasha basin in Kenya. Data envelopment analysis, general algebraic and modeling system, and Tobit regression methods were used in analyzing cross sectional data from a sample of 201 small scale irrigation farmers in the lake Naivasha basin. The results showed that on average, the farmers achieved only 63 % technical efficiency and 31 % water use efficiency. This revealed that substantial inefficiencies occurred in farming operations among the sampled farmers. To improve water use efficiency, the study recommends that more emphasis be put on orienting farmers toward appropriate choice of irrigation technologies, appropriate choice of crop combinations in their farms, and the attainment of desirable levels of farm fragmentation.
authors: Center for Development Research (ZEF), University of Bonn, Walter Flex Street, 53113, Bonn, Germany, ginawams2004@yahoo.com.
authors: Georgina W
authors: GW
authors: Njiraini
authors: Paul M
authors: PM
authors: Guthiga
mesh_terms: D057914:Agricultural Irrigation / Q000379:methods* / Q000706:statistics & numerical data*; D000383:Agriculture / Q000706:statistics & numerical data*; D004526:Efficiency; D006801:Humans; D007630:Kenya; D008962:Models, Theoretical; D012044:Regression Analysis; D014881:Water Supply / Q000706:statistics & numerical data*