Two stages method-based on Africa smart irrigation system assessment for willingness to pay: A case of Ghana Northern Region

Abstract

In northern Ghana, irregular rainfall poses a significant challenge for farmers. This research aims to investigate the readiness and ability of farmers in this area to invest in a smart irrigation system to tackle this problem and improve agricultural output. The research is motivated by the adverse effects of unpredictable rainfall patterns and droughts on agricultural yields in the area. To identify the key factors influencing farmers’ willingness to pay (WTP) and their maximum WTP for the smart irrigation system, the study utilizes the contingent valuation method (CVM). An integrated Bonferroni mean (BM), best-worst method (BWM) and technique for order preference by similarity to the ideal solution (TOPSIS) have been developed to evaluate these factors and rank the smart irrigation system options. A two-stage approach was proposed to account for the interrelationships among the WTP factors by integrating the Bonferroni mean (BM). Primary data was collected through a thorough survey involving 375 respondents from 125 households and 5 agricultural experts. The findings reveal that the maximum WTP for the smart irrigation system in Zabzugu District was GHS 628. Among the key factors of WTP, income level is the most significant factor and market condition is the least important factor. The effectiveness of the proposed method is demonstrated by ranking various smart irrigation system options. Weathermatic smartline was identified as the preferred choice and Galcon smart irrigation controllers as the least option. This study contributes to Ghana’s existing irrigation system literature and provides valuable insights for policymakers concerning sustainable agriculture.