ANALISIS PENDAPATAN USAHATANI PADI DI DESA JOGOPATEN, KECAMATAN BULUSPESANTREN, KABUPATEN KEBUMEN

Etika Dwi Hutami, S. Santoso, M. Handayani
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Abstract

This study aims to analyze the correlation between land area, seeds, fertilizers, pesticides, and labor with rice farming income in Jogopaten Village, Buluspesantren District, Kebumen Regency. The research was conducted from January to February 2022, in Jogopaten Village. The location selection was done purposively. The method used was a survey method. Sampling was done by simple random sampling, namely rice farmers as many as 60 respondents. The data analysis method used was quantitative descriptive analysis with the calculation of production costs, revenues, income, and pearson correlation analysis. Based on the results of the study, the variables of land area, seeds, fertilizers, and pesticides have a positive and significant correlation with farmers' income, while the variable days of work (HOK) have a negative and significant correlation with income. Variables of land area, fertilizers, and pesticides have a very strong correlation with income. Pearson correlation value between the land area variable and income is 0.994, so the two variables have a very strong correlation. Pearson correlation value between the fertilizer variable and income is 0.986, so it has a very strong correlation. Pearson correlation value between pesticide variable and income is 0.907, so it has a very strong correlation. The seed variable has a low correlation with income, this is evidenced by the low pearson correlation value of 0.356. The variable day of work (HOK) has a very low correlation with income, this is evidenced by the very low pearson correlation value (-0.151).
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本研究旨在分析克布门县Buluspesantren区Jogopaten村土地面积、种子、肥料、农药、劳动力与水稻种植收入的关系。该研究于2022年1月至2月在Jogopaten村进行。地点的选择是有目的的。使用的方法是调查法。抽样采用简单随机抽样,即稻农多达60名应答者。数据分析方法为定量描述性分析,计算生产成本、收入、收入和pearson相关分析。研究结果表明,土地面积、种子、化肥、农药等变量与农民收入呈显著正相关,而劳动日数(HOK)变量与农民收入呈显著负相关。土地面积、化肥和农药等变量与收入有很强的相关性。土地面积变量与收入的Pearson相关值为0.994,两者具有很强的相关性。肥料变量与收入的Pearson相关值为0.986,相关性很强。农药变量与收入的Pearson相关值为0.907,相关性很强。种子变量与收入的相关性较低,pearson相关值较低,为0.356。可变的工作天数(HOK)与收入的相关性非常低,这可以通过非常低的pearson相关值(-0.151)来证明。
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