Maize Productivity Optimization: An Analysis of Hybrid, Cobb-Douglas, and CES Models

Publication Type

Journal Article

Publication Date (Issue Year)

2026

Journal Name

Innovations and Interdisciplinary Solutions for Underserved Areas

Abstract

This study investigates the optimization of maize farming productivity using three different models: Cobb-Douglas, Constant Elasticity of Substitution (CES), and a hybrid optimization model. Data on farm inputs and output were collected from Northeast Nigeria between October and December 2023. The study compares the performance of these models in predicting optimal maize yields based on labour, fertilizer, herbicides, pesticides, seeds, and land inputs. While the Cobb-Douglas model demonstrated moderate yield predictions, it resulted in unrealistic input allocations, especially for seed quantities. The CES model provided greater flexibility in input substitution but underperformed in yield optimization due to calibration challenges. In contrast, the hybrid optimization model integrated strengths from both approaches, achieving the highest yield of 6.5t/ha with practical and balanced input distributions. This model outperformed both the Cobb-Douglas and CES models, highlighting its potential for improving agricultural productivity through better optimization and real-world applicability in resource-limited regions like Northeast Nigeria.

Keywords

Maize Productivity Optimization, Analysis, Hybrid, Cobb-Douglas, CES Models

Rsif Scholar Name

Daniel Dzarma Ezra

Rsif Scholar Nationality

Nigeria

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

Université d'Abomey-Calavi, Benin

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