Development and Statistical Evaluation of Fortified Soil Nutrient Formulation from Sugarcane Leaves Biomass for Soil Amelioration Catherine Ejieji, Elijah Alhassan, Joshua Olanrewaju Olaoye, Musliu Olushola Sunmonu, Qudus Babatunde Adeyi Yuzuncu Yil University Journal of Agricultural Sciences, 2026 Soil nutrient management is vital to sustainable farming practices and productivity. This study focused on pelletizing sugar cane leaves into granular fortified organic fertilizer for soil amelioration. To achieve this, a hammer mill incorporated with a cyclone was used for size reduction of substrate biomass. This was pelletized and characterized for its proximate and mineral compositions to confirm its potency for enhancing soil quality. Three sample sizes, namely coarse, medium, and fine, were formulated with the addition of NPK (20:10:10) fertilizer using liquid starch as a binder. A pelletizer having a ring die of diameter 6 mm was used for sample production. Proximate analysis established the elemental nutrient composition of the bio-based fertilizer, including nitrogen, potassium, calcium, magnesium, phosphorus, carbohydrate, protein, moisture content, crude fibre, fat, and ash. Additionally, sample durability was measured using a tumbling apparatus. At a confidence level of P<0.05, experimental results revealed that all nutrients except nitrogen were present in significant quantities, with nitrogen being less abundant in the plant leaves. Furthermore, particle size influence nutrient composition, with potassium displaying the most pronounced differences. The durability results revealed that the medium sized substrate has the highest durability index (DI) of 76.35% at 10.89 mc, followed by the fine particle size, 65.78% at 11.21 mc and coarse having 56.72% at 11.47 mc. Conclusively, these findings highlight the potential of sugar cane leaves as a fortified biomass organic fertilizer. Addressing the nitrogen deficiency identified in the substrate is crucial, supporting a mixed soil ameliorating protocol for robust soil quality.
Agricultural Model for Allocation of Crops Using Pollination Intelligence Method C. N. Ejieji, A. E. Akinsunmade Applied Computational Intelligence and Soft Computing, 2020 An agricultural model for allocation of crops is considered in this work using Pollination Intelligence Method. The model was constructed to solve farmer’s decision making in allocating crops to a piece of land using market price, known yield of crops, cost incurred during planting, and the total amount of land available. A new class of metaheuristic method called Flower Pollinated Algorithm is also presented in this work to solve the designed model. An improved version of the Flower Pollinated Algorithm called Pollination Intelligence Algorithm using an iterative scheme to override the switch parameter in Flower Pollinated Algorithm is also presented and used in solving the designed model. A case study of a farmer in Ife, Osun State, Nigeria, was used to implement the model, and the results obtained suggested that instead of allocating crops to land randomly based on farmer’s intuition, cost of planting, yield of crops, and market price were factors that must be considered by farmers for optimal profit before planting crops.
Order of schlictness of certain linear sums Applied Mathematics E Notes, 2015
New classes of analytic functions A.T. Oladipo, O.A. Fadipe-Joseph, C.N. Ejieji International Journal of Pure and Applied Mathematics, 2013