尼日利亚卡诺州农村基础设施对水稻生产力影响的数据筛选和初步分析

Y. Tanko, C. Y. Kang, R. Islam
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引用次数: 1

摘要

尽管拥有广阔的适合种植水稻的农田,但该国的当地产量却很弱,特别是在卡诺州,该州拥有最丰富的用于种植水稻的农田,也是该国36个州中最广泛的水稻种植者。因此,尼日利亚每年必须进口超过4毫米/吨的碾米,以补充国内生产。经济无法维持大米进口,因为它依赖于原油收入;因此,导致大米供不应求,价格过高。该研究是在2018年雨养和灌溉水稻的种植季节进行的,旨在确定尼日利亚卡诺州农村基础设施对稻农生产力的影响。该州有7个地方政府和17个水稻集群在种植水稻。采用多阶段有目的抽样方法,从135,895名稻农中随机抽取9个稻群中的768名稻农。利用SPSS (Statistical Package for Social Science)第22版软件进行数据筛选和初步分析,旨在满足多元分析的假设。因此,进行缺失数据分析以识别单变量异常值和多变量异常值。同样,正态性、偏度和峰度以及多重共线性问题也进行了检查。初步分析表明,该数据符合多变量分析的条件,适合进行推断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Screening and Preliminary Analysis on the Impact of Rural Infrastructure on Rice Productivity in Kano State, Nigeria
Despite having vast farmland suitable for paddy rice farming, local production in the country is weak, especially in Kano where the state has the most abundant farmland put to rice farming and the most extensive rice farmers in the 36 states of the country. As such, over 4mm/t of milled paddy rice has to be imported annually into Nigeria to supplement home production. The economy cannot sustain rice import because it depends on crude oil revenue; thus, leading to scarcity of rice at an exorbitant price. The study was conducted in the 2018 cropping season for rainfed and irrigated paddy rice, to identify the impact of rural infrastructure on the productivity of rice farmers in Kano State, Nigeria. There are seven local governments with 17 rice clusters in the state that are cultivating rice. A random sample of 768 rice farmers was selected in 9 rice clusters from the population of 135,895 rice farmers using multistage and purposive sampling. Using the Statistical Package for Social Science (SPSS) software version 22, data screening and preliminary analysis was conducted, aimed at satisfying the assumptions of the multivariate analysis. Thus, missing data analysis was performed to identify univariate outliers and multivariate outliers. Likewise, normality skewness and kurtosis, as well as multicollinearity issues, were checked. The preliminary analysis indicates that the data fulfil the conditions of multivariate analysis, thus, suitable for inferences.
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