EFRI DCheM: Distributed Photosynthetic Recovery of Livestock Waste Nutrients for Sustainable Production of Fertilizers

Dairy farming in the U.S. is a multi-billion-dollar industry that provides essential food products. At the same time, the millions of animals that this industry oversees generate a massive environmental footprint affecting air, land, and water quality. Specifically, livestock manure is a carbon- and nutrient-rich (in nitrogen and phosphorus compounds) waste stream that is routinely used as fertilizer.

This practice enables nutrient recycling but also leads to emissions of potent greenhouse gases such as methane and nitrous oxide, and to nutrient accumulation in soils due to manure nutrients often being imbalanced with respect to crop needs. Nutrient accumulation in turn promotes runoff to surface and groundwaters and leads to eutrophication and algae blooms that impact property values, recreation, and tourism.

Recovering manure nutrients in a scalable manner remains a grand societal challenge; the main difficulty is that manure is a vast, diluted, and distributed waste stream. To give some perspective, there are 1.2 million dairy cows in Wisconsin distributed across 9,000 dairy farms; a total of 24 million tons of manure are generated in the state annually and this waste stream contains 32,000 tons of phosphorous.

This project will seek to develop low-cost, modular, and flexible manure processing technologies to tackle this challenge. These processes will capture nutrients from manure using photosynthetic microorganisms (cyanobacteria) that will be engineered using synthetic biology techniques to tailor their performance for this application. We will combine experiments, computational models, and machine learning techniques to investigate the potential of using the cyanobacteria as sustainable biofertilizers that can help reduce the use of synthetic fertilizers and mitigate nutrient pollution of waterbodies.

The processes that we envision provide a step towards more sustainable farming and can potentially activate a bioeconomy that helps farmers access new technologies and revenue sources. This project also provides exciting opportunities to engage K-12, undergraduate, and graduate students in STEM fields.

The overall goal of this project is to develop photosynthetic processes for on-farm biofertilizer production from manure using cyanobacteria (CB). These multi-functional processes aim to: (i) produce a range of valuable biofertilizers in the form of wet/dry CB biomass and of nutrient-balanced CB-manure blends, (ii) recover manure nutrients for redistribution, and (iii) enable sustainable management of water, carbon, and energy in biofertilizer production.

These objectives will be achieved via integration of modular bag photobioreactors with manure anaerobic digestion units, biogas purification systems, CB biomass separation units, and power generators. The enablers of this integration will be engineered CB strains that: (i) maximize nutrient recovery from manure, (ii) facilitate crop nutrient uptake, (iii) maximize biogas production from manure, and (iv) facilitate biogas purification.

CB culture, co-digestion, and soil experiments will be guided using machine learning algorithms; these algorithms will aim to strategically collect data to create and refine process models. We will use our models to conduct techno-economic and life-cycle studies and to assess infrastructure-level benefits that result from the deployment of our processes (e.g., geographical nutrient balancing).

Likewise, the techno-economic modeling work will be used to compare the economic costs of current nitrogen and phosphorous containment strategies to the costs associated with potential risks of releasing the engineered CB to the environment. We have assembled a multi-disciplinary team at UW-Madison with expertise in systems engineering, synthetic biology, agricultural sustainability, and soil science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.