NDLI: Efficient Improvement of Silage Additives by Using Genetic Algorithms

Content Provider	PubMed Central
Author	Davies, Zoe S. Gilbert, Richard J. Merry, Roger J. Kell, Douglas B. Theodorou, Michael K. Griffith, Gareth W.
Copyright Year	2000
Abstract	The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives.
Starting Page	1435
File Format	PDF
ISSN	10985336
e-ISSN	10985336
Journal	Applied and Environmental Microbiology
Issue Number	4
Volume Number	66
Language	English
Publisher	American Society for Microbiology
Publisher Date	2000-04-01
Access Restriction	Open
Rights Holder	American Society for Microbiology
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Ecology Food Science Applied Microbiology and Biotechnology Biotechnology

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