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As the world’s population exponentially grows, more food production is required. This increasing food production currently has led to the un-sustainable production of chemical fertilizers and resultant overuse. A more sustainable option to enhance food production could be the use of fertilizer derived from food waste. To address this, we

As the world’s population exponentially grows, more food production is required. This increasing food production currently has led to the un-sustainable production of chemical fertilizers and resultant overuse. A more sustainable option to enhance food production could be the use of fertilizer derived from food waste. To address this, we investigated the possibility of utilizing a fertilizer derived from food waste to grow hydroponic vegetables. Arugula (Eruca sativa) ‘Slow Bolt’ and lettuce (Lactuca sativa) ‘Cherokee’ and ‘Rex’ were cultivated using indoor deep-flow hydroponic systems at 23 ºC under a photosynthetic photon flux density of 170 µmol∙m−2∙s−1 with an 18-hour photoperiod. Plant nutrient solutions were provided by food waste fertilizer or commercial 15:5:20 NPK fertilizer at the identical electrical conductivity (EC) of 2.3 mS·cm–1. At the EC of 2.3 mS·cm–1, chemical fertilizer contained 150 ppm N, 50 ppm P, and 200 ppm K, while food waste fertilizer had 60 ppm N, 26 ppm P, and 119 ppm K. Four weeks after the nutrient treatments were implemented, compared to plants grown with chemical fertilizer, lettuce ‘Rex’ grown with food waste fertilizer had four less leaves, 27.1% shorter leaves, 68.2% and 23.1% less shoot and root fresh weight, respectively. Lettuce ‘Cherokee’ and arugula grown with food waste fertilizer followed a similar trend with fresh shoot weights that were 80.1% and 95.6% less compared to the chemical fertilizer, respectively. In general, the magnitude of reduction in the plant growth was greatest in arugula. These results suggest that both fertilizers were able to successfully grow lettuce and arugula, although the reduced plant growth with the food waste fertilizer in our study is likely from a lower concentration of nutrients when we considered EC as an indicator of nutrient concentration equivalency of the two fertilizer types.

ContributorsCherry, Hannah Nichole (Author) / Park, Yujin (Thesis director) / Penton, Ryan (Committee member) / Chen, Zhihao (Committee member) / Environmental and Resource Management (Contributor, Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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As threats to Earth's biodiversity continue to evolve, an effective methodology to predict such threats is crucial to ensure the survival of living species. Organizations like the International Union for Conservation of Nature (IUCN) monitor the Earth's environmental networks to preserve the sanctity of terrestrial and marine life. The IUCN

As threats to Earth's biodiversity continue to evolve, an effective methodology to predict such threats is crucial to ensure the survival of living species. Organizations like the International Union for Conservation of Nature (IUCN) monitor the Earth's environmental networks to preserve the sanctity of terrestrial and marine life. The IUCN Red List of Threatened Species informs the conservation activities of governments as a world standard of species' risks of extinction. However, the IUCN's current methodology is, in some ways, inefficient given the immense volume of Earth's species and the laboriousness of its species' risk classification process. IUCN assessors can take years to classify a species' extinction risk, even as that species continues to decline. Therefore, to supplement the IUCN's classification process and thus bolster conservationist efforts for threatened species, a Random Forest model was constructed, trained on a group of fish species previously classified by the IUCN Red List. This Random Forest model both validates the IUCN Red List's classification method and offers a highly efficient, supplemental classification method for species' extinction risk. In addition, this Random Forest model is applicable to species with deficient data, which the IUCN Red List is otherwise unable to classify, thus engendering conservationist efforts for previously obscure species. Although this Random Forest model is built specifically for the trained fish species (Sparidae), the methodology can and should be extended to additional species.
ContributorsWoodyard, Megan (Author) / Broatch, Jennifer (Thesis director) / Polidoro, Beth (Committee member) / Mancenido, Michelle (Committee member) / School of Humanities, Arts, and Cultural Studies (Contributor) / School of Mathematical and Natural Sciences (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Ecology has been an actively studied topic recently, along with the rapid development of human microbiota-based technology. Scientists have made remarkable progress using bioinformatics tools to identify species and analyze composition. However, a thorough understanding of interspecies interactions of microbial ecosystems is still lacking, which has been a significant obstacle

Ecology has been an actively studied topic recently, along with the rapid development of human microbiota-based technology. Scientists have made remarkable progress using bioinformatics tools to identify species and analyze composition. However, a thorough understanding of interspecies interactions of microbial ecosystems is still lacking, which has been a significant obstacle in the further development of related technologies. In this work, a genetic circuit design principle with synthetic biology approaches is developed to form two-strain microbial consortia with different inter-strain interactions. The microbial systems are well-defined and inducible. Co-culture experiment results show that our microbial consortia behave consistently with previous ecological knowledge and thus serves as excellent model systems to simulate ecosystems with similar interactions. Colony patterns also emerge when co-culturing multiple species on solid media. With the engineered microbial consortia, image-processing based methods were developed to quantify the shape of co-culture colonies and distinguish microbial consortia with different interactions. Factors that affect the population ratios were identified through induction and variations in the inoculation process. Further time-lapse experiments revealed the basic rules of colony growth, composition variation, patterning, and how spatial factors impact the co-culture colony.
ContributorsChen, Xingwen (Author) / Wang, Xiao (Thesis advisor) / Kuang, Yang (Committee member) / Tian, Xiaojun (Committee member) / Brafman, David (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2022