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Study Evaluates Phosphorus Availability Underground using Plant Leaves as Biosensors3 min read

by Julie Bobyock on April 25, 2022

Climate and Ecosystem Sciences Division

Switchgrass – a native North American grass that grows in a range of harsh environments – is a biofuel that can help facilitate a transition to renewable energy. Berkeley Lab scientists are using the plants themselves as sensors to learn how to optimize its quality and production. [Photo Credit: Shutterstock]

When envisioning renewable energy, sources that often come to mind are the sun, wind, batteries, and water. However, biofuel, a type of renewable energy that converts organic material from plants into liquid fuel, is an important part of a global effort to achieve net-zero emissions. Switchgrass, a deep-rooted native North American grass that grows in a range of harsh environments, stands out as an alternative to other resource-demanding bioenergy crops such as corn and sugarcane. Although switchgrass can grow in soils with low nutrient availability, nutrient levels impact its yield and the amount of biofuel that can be produced. Phosphorus (P) is a non-renewable natural resource critical for plant growth, although intensive agriculture has depleted P in many soils. In P-limited soils, switchgrass roots work to access the critical nutrient and partner with their microbiomes to make the nutrient more available.

In collaboration with researchers from the Noble Research Institute, OK, and University of California, Berkeley, Berkeley Lab researchers studied switchgrass growth in Oklahoma soils with varying concentrations of P. Their recent work led by Zhao Hao published in Communications Biology used infrared spectroscopy methods and machine learning to investigate soil P by using switchgrass leaves as chemical biosensors, and identified how P availability impacts switchgrass chemical quality and biomass yield.

“Switchgrass can adapt to grow in soils with low nutrients and low water inputs, which makes it an important biofuel crop in North America,” Hao explained. “But we need to understand the mechanisms of this adaptation and its implications on the feedstock quality.”

The plant expends energy for roots and microbes to access soil P, which can adversely impact the yield and chemistry of shoots that are used for biofuel production. To mitigate this, P fertilizer is sometimes required –  however, its overuse and loss from ecosystems creates environmental issues such as harmful algal blooms. For this reason, monitoring plant available P in soil during plant growth could enable more efficient land management and biofuel production.

Using a machine learning model built with data from a laboratory-based experiment where P availability was highly-controlled, the team predicted soil phosphorus availability using signatures of leaf tissue chemistry. They applied this model to estimate how switchgrass P availability varied across two field sites with contrasting levels of phosphorus. They showed that, despite the differing soil phosphorus concentrations, the plants “sensed” similar amounts of phosphorus. The total yield of the switchgrass crop did not differ much across the soils, but the plant’s adaptation to increase P access in the lower P soils was shown from the chemistry of its shoots. This is because they had lower amounts of sugar polymers called cellulose relative to lignin, a polymer that is difficult to “digest” by biofuel producing microbes. Since cellulose is the primary ingredient of biofuels, adapting to lower nutrients might reduce the quality of the crop and its effectiveness as a biofuel.

These findings can help inform land-management practices through newly developed machine learning-techniques that predict phosphorus conditions, and also sheds insight into how phosphorus conditions influence biofuel crop quality. This opens the door to new strategies that can monitor biofuel crop chemical quality and nutrient delivery, optimizing biofuel production critical in the transition to a net-zero emissions future. 

News & Events

Chun Chang Places Second in Annual Berkeley Lab Pitch Competition3 min read

January 18, 2023

Commercializing Berkeley Lab inventions is an important part of the Lab’s mission, and one that requires strong communication skills. For example, Lab inventors need to be able to pitch their ideas to external partners and potential funders.  The annual Berkeley Lab Pitch Competition occurred on October 27, 2022 and is a part of an entrepreneurship…

EESA Scientists Collaborate With Universities to bring Environmental Science Research Opportunities and Training to Students Underrepresented in STEM3 min read

January 13, 2023

  EESA researchers are collaborators in three of the 41 projects awarded in December by DOE through its Reaching a New Energy Sciences Workforce (RENEW) initiative.  RENEW aims to build foundations for research at institutions that have been historically underrepresented in the Office of Science (SC) research portfolio. The initiative provides opportunities for undergraduate and…

New Report Explores Revolutionary Environmental Artificial Intelligence Infrastructure5 min read

January 10, 2023

In a collaborative effort between the U.S. Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) and DOE’s Advanced Scientific Computing Research (ASCR) program, as well as with community experts, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held from October through December 2021. BER developed the process as the Model-Experiment paradigm, or ModEx, and a report released this fall outlines the key takeaways of last year’s event.

A Q&A With Postdoc Kunxiaoja Yuan3 min read

January 4, 2023

  Kunxiaojia Yuan received her Bachelor’s of Engineering in remote sensing and Ph.D. in geographic information engineering from Wuhan University. She is a postdoctoral researcher in EESA, with a research focus on global carbon, energy, and water cycle analysis and model evaluation using machine learning and causal inference. What motivated you to pursue a postdoc…

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