Use of linear programming models in experimentation with plant nutrients

Abstract

Nutrition is an important issue of plant cultivation and experimentation with plant nutrients is a supporting tool for agriculture. However, use of high purity grade reagents as nutrient sources can be expensive and increases the cost of an experiment. The objective of this study was to minimize the acquisition cost of high purity grade reagents in experiments on plant nutrient deficiency by using the missing element technique through linear programming models, and to generate recommendation tables for preparation of culture solutions, as well as to quantify gains through a simulated experiment. Two linear programming models were formulated containing concentration constraints for each nutrient in the culture solution. Model A was based on 16 reagents for preparation of the culture solution, while model B was based on 27 reagents, looking to increase choice options. Results showed that both models minimized the acquisition cost of reagents, allowing a 9.03% reduction in model A and a 25.98% reduction in model B. The missing sulfur treatment proved the most costly for reagent acquisition while the missing nitrogen treatment proved the least costly. It was concluded that the formulated models were capable of reducing acquisition costs of reagents, yet the recommendations generated by them should be tested and checked for practical viability.