ArticleJournal of economic entomology2026
Life-history traits and reduced variation of mass-reared black soldier fly (Diptera: Stratiomyidae) larvae fed dairy manure with Rhodococcus and Paenibacillus spp.
Article in Journal of economic entomology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Manure is an abundant waste source in dairy production with disposal creating significant challenges for producers. Recent work has shown that black soldier fly, Hermetia illucens (Linnaeus) (Diptera: Stratiomyidae), larvae can digest dairy manure; however, its high cellulose content and low nutrient value impact larval performance. Adding probiotics to black soldier fly feed can increase feed digestibility and weight gain. The goal of this study was to determine the influence of probiotic additions on black soldier fly larval performance when fed dairy manure. This study evaluated life-history traits of black soldier fly larvae fed dairy manure with single or combined probiotic treatments: Rhodococcus sp., Paenibacillus sp., and 1:1 combination, compared to a no-microbe control. Larvae were mass-reared on fresh dairy manure, and key metrics-bioconversion rate, survivorship, total biomass, and larval weight-were compared across treatments. Results showed a significant trial effect for all variables measured. Mean peak weights and the largest larval weights were 3.5-20.0% greater in at least one probiotic treatment compared to the control, while other life-history traits did not differ significantly. However, probiotic treatments reduced variation up to 17.0% in black soldier fly larval weight across trials, a trait not historically discussed in black soldier fly research. Developing processes that optimize larval weight in combination with reducing variation are critical for developing precise business models for predicting future production.
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