Unlocking the potential of small-scale irrigation for small farmers in Northwestern Ethiopia

PurposeThe main purpose of this study is to assess the impact of small-scale irrigation on the total income of small farm households. Additionally, it aims to identify the factors influencing the total income of households who have adopted small-scale irrigation methods.Design/methodology/approachThe study utilized cross-sectional data sourced directly from primary channels. Primary data collection was facilitated through the administration of semi-structured questionnaires and conducting in-depth interviews. Both propensity score matching (PSM) and an ordinary least squares (OLS) model were employed to assess the impact of small-scale irrigation on total income and to identify factors affecting the effectiveness of SSI. In order to ensure that the PSM model’s findings were consistent, we used instrumental variables (IV) estimation, which was conducted using two stages least squares (2SLS) regression.FindingsThe study findings reveal that small-scale irrigation adopters earn, on average, 12,947.32 Birr more than non-adopters. The PSM model reveals that households engaged in small-scale irrigation experience a significant increase in total income compared to non-irrigating households. The study underscores the need to address key factors influencing the treatment effect on household income, including distance to farm training centers, proximity to water sources, access to transportation, availability of family labor and frequency of extension contact.Research limitations/implicationsThis paper only examines the effect of small-scale irrigation on small farm households’ total income. This study encourages future studies to investigate the effects of small-scale irrigation on small farm household food security, total spending, poverty reduction, welfare and net earnings in the study area.Originality/valueMost studies only analyze how small-scale irrigation affects the total income of small-scale farm households. This study fills a knowledge gap by identifying the key elements that influence the extent of the impact on user households.