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Wackett, L.
USDA - Agricultural Research Service
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The overall goal is to combine plant growth and yield studies, plant microbiome composition, chemical synthesis, biochemistry, database development methods, and educational methods to enhance our understanding of the fate and effects of urea-type compounds in agriculture.A major early goal is to use already acquired data on ~50,000 plant and soil associated microbial genomes and use a method of machine learning known as decision tree classification. Here we will use random forest classification of environmental soil and microbial community characteristics to predict the biodegradation capacity of urea-type compounds in agro-ecosystems. We will examine soil type, pH, sampling location and presence of other microbial community members known to contain urea biodegradation pathways such as certain species of Rhizobium, Rhodococcus, and Herbaspirillum will be the most important predictors for the capacity to degrade urea compounds within a given microbe. The source code and random forest model will be made publicly available for interactive, web-based use on the Urea Fate & Effects database. We anticipate our classifier may be applied to predict the biodegradation capacity of ureide compounds in new U.S. agricultural sampling locations where expensive shotgun metagenomic sampling cannot be conducted. Ultimately, we aim to predict biodegradation capacity of different urea compounds to better inform N-amendment practices for farmers so they increase profit margins, maximize yields and limit nutrient run-off. Another major goal is to conduct greenhouse pot experiment using 15N-labeled dicarbamylurea and urea (10 atom %) to determine the effects of dicarbamylurea on plant growth and N supply in comparison to a standard urea fertilizer. Pending those results, field studies will be conducted with corn and potato. Prior to planting, the soil will be amended with P, K, secondary and micro-nutrients according to soil test recommendations. Weeds, insects and disease will be controlled based on standard practices for the region. At harvest, grain and stover will be sampled for corn. Tubers and vines will be harvested for potato. Potato tubers will be graded for size and quality. Samples of stover, grain vines and tubers will be collected for dry matter and N concentration.One of the major goals of the proposed research is to use data mining and bioinformatics approaches to predict the biodegradation capacity of urea-type compounds in agro-ecosystems and interface that with experimental data. In that context, research is proposed to study the effect of ureide-degrading bacteria on plant growth to gain insights into potential enhancement practices for agricultural productivity. We will address these questions by specifically investigating how bacteria containing dicarbamylurea degrading-enzymes impact the growth of corn and potato plants.Another important goal is to synthesize chemicals to support the overall experimental research. Labelled and unlabelled dicarbamylurea will be synthesized via reaction of urea with urea dihydrochloride. We have found the final product mixture to be amenable to granulation into a particle containing slow-release dicabamylurea, trapping fast-release NH4Cl in a 2:1 ratio and delivering 38% N by weight, which can make an ideal fertilizer mixture.We have a goal to implement and develop the Urea Fate and Effects (URE) Database. A prototype of the Urea Database is currently on-line.It will be further developed as part of this project with a focus on furthering an understanding of the fate and effects of urea compounds on plants and microbes. We will also construct a relational database with fields of data on chemical compounds, use statistics, plants, microbes, genes, and enzymes. As in our previous database models, data such as DNA sequences and enzyme characteristics will be provided by links to well-curated databases such as GenBank and the Protein Data Bank (PDB).Dr. Martinez-Vaz will develop a multi-week laboratory project at Hamline University, a major goal of our project. During the laboratory project, undergraduate students will participate in "crowdsourcing science" to generate data that will be useful to expand and enhance the information in the Urea Fate and Effects (URE) Database. Instructional protocols and assessment data will be disseminated to the educational community through a publication in open access science pedagogy journals such as Coursesource and the Journal of Microbiology and Biology Education.
Funding Source
Nat'l. Inst. of Food and Agriculture
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Education and Training