Browsing by Author "Nanayakkara, K.G. Madurika"

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    Measurement Model to Assess Sustainable Agriculture Potential of Sri Lankan Rice Farmers Derived Using Rural Livelihood Assessment Framework: Studied in Mahaweli-Block (H)
    (Faculty of Graduate Studies, University of Kelaniya, Sri Lanka., 2024) Ariyarathne, S.M.W.P.K.; Nanayakkara, K.G. Madurika; Thushara, S.C.
    This paper describes a systematic method that identified constructs and indicators of a measurement model to assess rice farmers' Sustainable Agriculture Potential (SAP). The method used the Rural Livelihood Assessment Framework (RLAF) definition to define SAP's primary constructs. The capital assets defined in RLAF (human, social, financial, physical, and natural) were then explained using previous Sustainable Agriculture (SA) research findings. An initial 130 indicators were framed into five-point Likert scale questions to form a research questionnaire. The questionnaire was initially administered among 64 selected farmers in a dominant rice cultivation region (Mahaweli Block H) in Sri Lanka. The responses were analyzed using the measurement model analysis technique using the Partial Least Square Structural Equation Modelling (PLS-SEM). Based on this analysis, 87 questions were identified as effective measures of the farmers' SAP. The refined questionnaire was surveyed among 386 rice farmers randomly selected in the same region. The responses were analyzed using the PLS-SEM techniques for each capital asset in the form of measurement models. The analysis proved those 87 questions (indicators) are productive and can explain farmers' SAP compositely. The researcher believes the model will be helpful for future researchers in assessing the strengths of SAP and the nexus between SAP and other variables, such as their ability to adopt more organic-centric farming and resilience to other varying factors impacting their farming. Furthermore, the method used to maximize the variance explained in developing indicators and ruling out the less productive indicators could be insightful for researchers in future studies.