Pagar Alam Coffee is a Besemah coffee originating from the Smallholder Plantation in South Sumatra, Indonesia. The majority of Pagar Alam coffee farming is a hereditary business. Coffee farmers' income is very dependent on coffee production, production costs, and coffee prices. This study aims to obtain a probability model of Pagar Alam coffee farmers income based on the factors that influence it. The independent variables studied were the number of dependents, economic conditions, number of trees, age of trees, frequency of fertilizer used, frequency of pesticide used, production at harvest time, production outside harvest time, number of women workers outside the family, minimum price of coffee, maximum price of coffee, farmers' gross income, and land productivity. Modeling used binary logistic regression method on 179 respondents. There were three methods used, i.e. enter method, forward and backward methods. The model using enter method results the greatest prediction accuracy which is 87.7%. The factors that have a significant influence on the net income of Pagar Alam coffee farmers are gross income, land productivity, and the number of women workers from outside the family. The most influential variable is gross income.
Pagar Alam coffee farming is a smallholder plantation, the majority of which is a hereditary business. The success of this coffee farming cannot be separated from existing resources, including land productivity. Land productivity concerns the amount of production, land resources, and land management efforts. This paper discusses the factors that influence the land productivity of coffee farms in Kota Pagar Alam, using binary logistic regression analysis. In general, there are 5 factors discussed, namely the identity of farmers and their internal factors, agricultural land, the performance of farmers in the production process, yields, and external factors on the productivity of Pagar Alam coffee farms. The data used are 191 respondents with 33 independent variables and one dependent variable. Each variable is divided into categories. Land Productivity as the dependent variable is divided into 2 categories, namely low and high. Based on bivariate analysis, variables related to land productivity are land area, number of trees, frequency of fertilizer used, frequency of pesticides used, length of harvest, production, female labor in the family, gross income, net income, and production costs. Furthermore, based on the binary logistic regression model of land productivity probability, variables that significantly affect land productivity of Pagar Alam coffee farms are area, number of trees, crop production, and net income. The accuracy of the model simultaneously was 93.2%. The probability value of the model is predominantly influenced by the harvest production variable with an odds ratio of 49.505. If the category of harvest production and net income increases, the probability for high land productivity will also increase. Conversely, if the area of land and the number of trees increases, the probability of high land productivity will decrease.
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