The causal-effect model of input factor allocation on maize production: Using binary logistic regression in search for ways to be more productive

Abstract

This study delved into the impact of input factor allocation on maize production, seeking to identify strategies for enhancing productivity. The primary data for this study were collected from maize farmer households and analyzed using the binary logistic regression model (BLRM). Consequently, the simultaneous examination of the twelve independent variables using the BLRM yielded a noteworthy impact on maize production. Meanwhile, five of the twelve variables examined in part revealed a significant and positive impact on maize production. The variables that demonstrated statistically significant positive relationships consisted of land area, labor, urea fertilizers, insecticides, herbicides, and labor. Then, seed application and NPK fertilizer variables had a negative and significant influence, whereas the other variables did not. The results of this study indicate that some production factors employed by farmers significantly affected maize yield. This information may be used to develop a practical strategy for enhancing productivity in maize farming. Some practical implications in immediately oriented recommended: the expansion of maize farming area managed by farmers and the increase in the volume of application of urea fertilizers, insecticides, and herbicides, as well as the increase in the number of workers in maize farming, are expected to encourage an increase in maize production. The results of this research also contribute to the existing knowledge by providing empirical evidence that clarifies the positive and significant impact of various production inputs, which include land area, urea fertilizers, insecticides, herbicides, and labor, on maize production and productivity. It also supports the objective of promoting more efficient farming practices.