Abstract
This study involved mixture experiment using simplex lattice design approach in cultivation of Maize crop with the view of optimizing the fertilizer components (dependent variables) on the output parameter (maize fodder). The objective of this study was to evaluate optimal sets of mixture of fertilizer components that could maximize the response variables of interest. Di-Ammonium Phosphate (DAP), Poultry manure, Sheep manure, and Farmyard manure components mixed in various proportions in accordance with simplex lattice design were applied in planting hybrid maize seeds. With the application of the special cubic statistical model formulated, it was found that farmyard manure and poultry manure produced the optimal fertilizer condition. However, the this study further provided specific optimal fertilizer blend for maize fodder production as 8.0 tons ha -1 of farmyard manure mixed with 1.212 tons ha -1 of poultry manure. Under these conditions, a maximum outputs 42 tons ha -1 of maize fodder were realized. The study concluded that the formulation of statistical model for crop production could be useful for prediction and evaluation of the effects of experimental factors. KEY WORDS : Maize fodder; Fertilizer components; Model; Mixture experiment; Simplex Lattice Design; DOI : 10.7176/MTM/9-7-05 Publication date : July 31 st 2019
Highlights
Response surface methodology (RSM), is a collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response (Montgomery 2000)
Mixture experiments are suitable to use when investigating if synergism exist in various blends of these fertilizer components
A four- fertilizer component design used in this study illustrates how mixture experiments can be applied in agricultural research
Summary
Response surface methodology (RSM), is a collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response (Montgomery 2000).Mixture experiments are commonly encountered in several fields, including the food, chemical, pharmaceutical, engineering and consumer products among many others. The design factors are the proportions of the components under study that sum to a constant, and response variable depends on these proportions. A number of mixtures experimental designs have been formulated but the most commonly applied is simplex-lattice design. It takes the shape of a triangle with the pure blends being located at the vertices of the triangle for a case of a three-component mixture. Interior points give blends of all the components while data collected at the midpoint of the edges of the triangular surface gives the response for the binary blends. For a four-component mixture it takes the form of a tetrahedron
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
