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Authors: Mohamed Ahmed Al-Feel and Seram Kamal Mohamed Abdullah
Emails: Mohamed Alfeel: mohamedalfeel@gmail.com
Keywords: Strata, plot, random sample, analysis of variance, standard error.
JEL Codes: D6, I3, P4, Q12.
Issue Date: 17.07.2013
Series no.: Agricultural Economics Working Paper Series 2 (2013).
Abstract:
This study was carried out to evaluate the effects of different plots sizes on the estimation of wheat yield in New-Halfa scheme. We applied crop cutting method to estimate wheat yield and multi-stage stratified sampling method for the sampling. We use a farm survey data with different plot sizes, in the four villages within the Scheme, and for season 2009/2010. The analysis of variance (ANOVA), multiple comparison, standard deviations, standard errors, and coefficients of variation were used in analyze the results. Results showed no significant differences between large and medium size plots in yield estimation. However, there were significant differences between large and small plot sizes and between medium and small plot sizes with respect yield estimation. Higher crop yields were obtained in small compared to large and medium size plots in both strata. As plot size increases, the estimated yield and standard deviation of yield decreases in the two strata. The yield estimation attains a stable value when the plot size is significantly large and it is not recommended to estimate wheat yield with plot sizes less than 42 square meters.
2. Title:The Prevalence of Poverty and Inequality in South Sudan: The Case of Renk County
Authors: Ahmed, Adam; Roghim, Somaia; Saleh, Ali and Khalid Siddig.
Emails: Adam Ahmed: adamelhag2002@yahoo.com
Keywords: Poverty, Inequality, South Sudan.
JEL Codes: D6, I3, P4, Q12.
Issue Date: 27.01.2013
Series no.: Agricultural Economics Working Paper Series 1 (2013).
Abstract:
In this study we use a comprehensive household income and expenditure survey with a sample of 245 respondents representing urban and rural households in the Renk County of South Sudan to assess the prevalence of poverty and inequality in the study area. We used the cost of basic needs; to establish both food poverty line and Poverty line; estimated poverty incidence, gap and severity; and estimated different equality measures. Major results show that 87% and 73% of the urban and rural households respectively fall below our calculated poverty lines. The estimated Gini coefficient was 18% and 20% for urban and rural households, respectively. Results of other equality measures show higher inequality between the poorest and richest segments of households as the richest quintile among urban households consumes 5 times that of the poorest, while that of the rural households consumes 4 4 folds the poorest quintile.
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