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Nurul Fadillah Nur
Universitas Puangrimaggalatung
Agriculture, Biological Sciences & Forestry

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THE EFFECT OF PRODUCTION FACTORS ON RICE PRODUCTIVITY Nurul Fadillah Nur; Asrijal Asrijal; Fitry Purnamasari; Sri Hardianti Rosadi
Agribusiness Journal Vol 6, No 1 (2023): Agribusiness Journal
Publisher : UNIVERSITAS SEMBILANBELAS NOVEMBER KOLAKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (316.158 KB) | DOI: 10.31327/aj.v6i1.1968

Abstract

This study aims to determine the effect of factors of production on rice productivity. The number of respondents in this research were 31 farmers who were taken randomly. This study used proportional parameter testing and multiple linear regression analysis using the Ordinary Least Square (OLS) method. This research has normal distribution, free from multicollinearity and heteroscedasticity problems multiple linear regression analysis can be performed. The results of the analysis show that the results of the R2 test show that 83.9% of the variation in rice productivity can be explained by the five independent variables while the remaining 16.1% is explained by other factors not included in the model. The results of the F test show that the independent variables jointly affect rice productivity. The results of the t test showed that land area, seeds, labor, pesticides had a significant positive effect, while fertilizer had a significant negative effect on rice productivity.
THE EFFECT OF PRODUCTION FACTORS ON RICE PRODUCTIVITY Nurul Fadillah Nur; Asrijal Asrijal; Fitry Purnamasari; Sri Hardianti Rosadi
Agribusiness Journal Vol 6, No 1 (2023): Agribusiness Journal
Publisher : UNIVERSITAS SEMBILANBELAS NOVEMBER KOLAKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31327/aj.v6i1.1968

Abstract

This study aims to determine the effect of factors of production on rice productivity. The number of respondents in this research were 31 farmers who were taken randomly. This study used proportional parameter testing and multiple linear regression analysis using the Ordinary Least Square (OLS) method. This research has normal distribution, free from multicollinearity and heteroscedasticity problems multiple linear regression analysis can be performed. The results of the analysis show that the results of the R2 test show that 83.9% of the variation in rice productivity can be explained by the five independent variables while the remaining 16.1% is explained by other factors not included in the model. The results of the F test show that the independent variables jointly affect rice productivity. The results of the t test showed that land area, seeds, labor, pesticides had a significant positive effect, while fertilizer had a significant negative effect on rice productivity.
Co-Authors A. Asrijal Asrijal Asrijal Fitry Purnamasari Sri Hardianti Rosadi