The research proposal aims to discover new knowledge concerning the impacts of COVID-10 on Indonesian Farmers. Indonesia farmers are amongst the most vulnerable groups to the COVID-19 across the globe. Many farmers have recently risen above poverty; however, COVID-19 threatens to reverse decades of progress toward reducing poverty. The research examines the impacts and responses of farmers through the farmers' lens and aims to gain knowledge that will reverse the current trend and discover farm practices that will improve livelihoods in the long term. The survey data will be tailored to four journal publications; Indonesian farmers short and long-term responses to COVID-19; Indonesian farmers perceptions of risk during COVID-19; the roles of family and community support for Indonesian farmers during COVID-19; and the efficacy of Government protection and support provided to Indonesian farmers during COVID-19. Data will be obtained from three Indonesian locations, Bali, Lombok and Flores. Due to COVID-19 restrictions, Indonesian enumerators will be employed to conduct surveys instead of the author conducting the surveys. Enumerators will upload data daily and report through ZOOM when necessary. Enumerators will use the Kobo Toolbox App. Kobo Toolbox is currently in use throughout the globe for all primary humanitarian survey-based research. Data will be analysed using SPSS. Analyses will commence with descriptive statistical analyses including measures of central tendency, frequency, measures of variation such as range, variance and standard deviation, and measures of position such as percentile ranks, and quartile ranks of ratios. Descriptive statistics are presented in tabular form, as graphical information or as numerical summaries of Data This will be followed by selective analyses using inferential statistics which may include confidence intervals, one sample hypothesis testing, contingency tables and Chi-Square Statistics, t-test, ANOVA (Analysis of variance), Pearson correlation, bi- and multivariate regression models
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