Title: Computational Approach of the Furrow for Optimized Nigerian Yam Ploughing
Authors: Gbenizibe Bonus Wombu
Volume: 9
Issue: 12
Pages: 219-231
Publication Date: 2025/12/28
Abstract:
: Yam is a crucial staple crop in Nigeria, which happens to be the largest yam producer in the world. However, growing it comes with its own set of agronomic and engineering hurdles. This study focuses on modeling ploughing operations-specifically "furrow" techniques-to optimize yam production across Nigeria's varied ecological zones. We take a closer look at yam agronomy and tillage practices, spanning from humid forests to savanna regions, and consider both rain-fed and irrigated systems. Interestingly, traditional manual mounding is still the norm, with around 96% of farmers in southwestern Nigeria preparing their land by hand. This method creates loose, raised seedbeds that are great for tuber development, but the labor-intensive nature of it limits expansion. To tackle this, we explore advanced computational methods for designing furrows, including the Finite Element Method (FEM), Discrete Element Method (DEM), Computational Fluid Dynamics (CFD)-like smoothed-particle hydrodynamics-and machine-learning models. These techniques are particularly useful for simulating how soil interacts with tools and predicting their performance. We also conduct a comparative sensitivity analysis to see how factors like soil texture, moisture content, tractor draft force, and plough geometry affect yam yield and operational costs. Our findings pull together recent field studies from West Africa (2015-2025) to quantify these impacts. We confirm that loose, fertile soils with just the right amount of moisture are essential for high yields, while optimal plough configurations-like the right depth and ridge spacing-can boost tuber yields by over 30-50%. Mechanized ridging stands out as a game-changer, allowing for higher planting densities and saving on labor, which ultimately enhances productivity and profitability compared to traditional mounds. We analyze cost metrics, using draft force and fuel/labor inputs as proxies, alongside yield outcomes to pinpoint scenarios that benefit farmers. To make our findings clear, we present model comparison matrices and schematic workflows that show how simulations and data-driven models can aid in decision-making. This study offers a comprehensive view across Nigeria, drawing on examples from various regions to ensure its relevance in different agro-ecological contexts. By combining agronomic knowledge with computational modeling, we showcase ways to enhance yam furrow design, leading to better yields and lower costs. This provides essential guidance for both researchers and practitioners working with tropical root-crop systems.