Abstract
Even though Ethiopia is endowed with natural resource, it is one of the poorest country in world. The reason for this is maluses of the natural resource and dependence on traditional and rain feed agriculture. Small scale irrigation is applied in Ethiopia more than 5 decades and in SNNP region 3 decades ago. This study was conducted in SNNP region with 60 small scale irrigation users and 343 non users. The objective of this study was to evaluate the impact of small scale irrigation on household on food security. To analyze the data descriptive statistics like chi2 for categorical variables and t-test continuous predictor variables was used. Propensity Score matching model was used to analyze the impact of the small scale irrigation on food security of the house hold. Based on this study application of small scale irrigation in the study area does not contribute to be food secured. This may be due to the region is getting high rainfall relative to other regions like Afar, Somali and Tigray region. The farmers does not want to west their time on irrigation activities doe to plenty of rainfall and precipitation. On the other hand, most of the farmers staple food is depend on Inset (falls banana) which does not need irrigation. Based on this study we recommend that, irrigation activities should be applied in areas by which rainfall is scarce like the regions Tigray, Afar and Somali region by which rain is scarce. Keywords : food index, Irrigation, PSM model, SNNP region DOI: 10.7176/ISDE/12-1-02 Publication date: January 31 st 2021
Highlights
This study focuses on the major parts of different irrigations schemes applied in SNNP regional state of Ethiopia
Objectives of the study The general objective of this study is to examine the impact of small scale irrigation on the Household food security of the users in SNNP regional state
Propensity Score Matching For more than two decades, advanced statistical methods known as propensity score (PS) techniques, have been available to aid in the evaluation of cause-effect hypotheses in observational studies
Summary
We use a standard setup in the treatment effect literature. Let us suppose we have a population of individual units under study indexed by i = 1, 2, ... , N, an indicator for a binary treatment, T, which assumes the value 1 for treated units and 0 for untreated, or controls, and an outcome variable, which we indicate by Y. Starting from assumption A.1, the basic idea is that within each cell defined by the values of the covariate X assignment to treatment or control group is random. If in a given application we are willing to assume that all relevant variables that affect the selection on treatment and outcome are collected in the set X (and we are confident that assumption A.1 holds) we can match each treated unit with one (or more) control unit with the same values of X. The reason is that the probability of observing a treated and a control unit with exactly the same value of the propensity score is, in principle, zero, since e(X) it is a continuous variable. Based on this sample frame, 60 users of irrigation and 343 non-user households were selected using proportionate to sample size for a face-to-face interview
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