E ISSN: 2583-049X
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International Journal of Advanced Multidisciplinary Research and Studies

Volume 6, Issue 1, 2026

An Economic Analysis of Agri-Business Growth Models in Maize Productions: A Case Study of Small and Medium Farmers in Chinsali Block B



Author(s): Chongo Setty, Dr. Chisala Bwalya

Abstract:

Agriculture remains a cornerstone of Zambia’s economy, contributing significantly to food security, employment, and rural livelihoods. Among staple crops, maize plays a central role in both consumption and income generation, particularly for small and medium-scale farmers. In Chinsali Block B, maize production represents a critical component of local agri-business activities, yet farmers face multiple challenges that affect productivity, profitability, and sustainability. The general objective is to analyze the dynamics of maize production in this context, focusing on the operational, technological, and financial factors that shape farmer outcomes. Specifically, the study examines input costs associated with maize production, evaluates the adoption and effects of advanced agricultural technologies and climate-smart agriculture practices, and assesses the profitability of maize production for small and medium-scale farmers. By understanding these factors, the study aims to inform strategies that can enhance efficiency, sustainability, and income generation in maize-based agri-businesses in Chinsali. The study targeted small- to medium-scale maize farmers in Chinsali Block B. A convenience sampling approach was employed to select participants, resulting in a sample of 50 farmers. Data collection was conducted using a semi-structured questionnaire that included both open-ended and closed-ended questions, administered through structured surveys and interviews. Data were entered and analyzed using STATA, with descriptive statistics presented in Microsoft Excel 365. For inferential analysis, ANOVA and regression were applied to examine associations between variables, while Chi-square tests were used to determine relationships among categorical variables. The study revealed considerable variability in both production costs and outputs. Seed costs averaged 848 ZMW per hectare, fertilizer 7,890 ZMW, labor 760 ZMW, land preparation 356 ZMW, harvesting 278 ZMW, and weedkiller 636 ZMW, while average maize yields were 137 kg per hectare, generating mean revenues of 41,300 ZMW. Regression analysis showed that land preparation and weedkiller costs positively influenced production outcomes, while higher labor costs negatively affected performance, with the model explaining 93.8% of revenue variation (R² = 0.938, p < 0.001). Adoption of modern farming technologies was high (84%), with tools such as precision agriculture and soil sensors used most frequently, and 64% of farmers reporting improved yields. Climate-smart agriculture (CSA) practices were adopted by 80% of respondents, predominantly crop rotation (64%) and agroforestry (28%), with effectiveness perceived as high by 40%. Profitability analysis indicated moderate profitability for most participants (66%), with maize contributing an average of 54% to total farm income. Government policies were cited as the largest factor influencing profitability (46%), and 64% of farmers planned to expand maize production in the next five years, reflecting overall optimism about future agri-business growth. The study recommends that Chinsali maize farmers optimize key input costs, expand the use of modern farming technologies and climate-smart practices, and access training to overcome knowledge and cost barriers. Supportive government policies and market access should be strengthened, while farmers strategically plan for sustainable production growth to improve yields and profitability.


Keywords: Artificial Intelligence, Project Performance, Construction Industry, AI Effectiveness, Technology Integration, Construction Project Optimization, AI in Construction

Pages: 377-387

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