About

Wendy Yang is a Professor in the Department of Plant Biology at the University of Illinois. She holds multiple leadership and affiliate roles including Deputy Director of Research & Development at the Center for Advanced Bioenergy and Bioproducts Innovation, Associate Director of the Agroecosystem Sustainability Center, and affiliations with the Program in Ecology, Evolution, and Conservation Biology, the Institute for Sustainability, Energy, and Environment, the Institute for Genomic Biology, and the Center for Digital Agriculture at the University of Illinois. Her research is situated within the Yang Lab, which focuses on Global Change Ecology and Stable Isotope Biogeochemistry. Through her leadership roles and research activities, Professor Yang contributes to advancing understanding and innovation in bioenergy, agroecosystem sustainability, and ecological and environmental sciences.

Research topics

• Biology
• Ecology
• Environmental science
• Computer Science
• Genetics
• Soil science
• Chemistry
• Economics
• Botany
• Biotechnology
• Natural resource economics
• Agroforestry
• Environmental chemistry
• Horticulture
• Biochemistry

Selected publications

• Retraction notice to “Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest” [Agricultural and Forest Meteorology 373 (2025) 110750]

  Agricultural and Forest Meteorology · 2026-01-09

  articleOpen access
  PublisherDOI

• Unlocking plant-microbial interactions in deep Mollisols in the Midwestern US: Linking depth gradients in roots, microbial activity, and soil carbon in agroecosystems

  Biogeochemistry · 2026-04-13

  articleOpen access

  Abstract Deep-rooted plants may build soil carbon (C) stocks, but most research has focused on shallow soils, leaving gaps in our understanding of how shifts in the balance between decomposition and C inputs drive soil C accumulation with depth. Thus, our objectives were to: (1) link depth gradients in root biomass with microbial activity and soil C stocks down to 1 m, and (2) examine the potential of simple C inputs to prime soil C across depths. To this end, we dug 5 quantitative soil pits in Argiudolls under mature perennial miscanthus plots in the SoyFACE Farm (Champaign-Urbana, IL). We added 13 C labeled glucose to our soils to determine the fate of simple C inputs with depth. We found that fine root biomass, total soil C, mineral-associated organic C (MAOC), particulate organic C (POC), and microbial activity (as measured by potential enzyme activity) declined with depth. POC declined more rapidly than MAOC, resulting in an increase in the ratio of MAOC-to-POC. Root biomass, enzyme activity (either acid phosphatase or n-acetyl-glucosaminadase) activity, and microbial respiration explained 74% and 38% of the variability in soil total C and MAOC, respectively, while POC was dependent on root biomass and microbial respiration (47%). Although the incorporation of simple 13 C inputs into MAOC was similar across depths, these inputs led to greater net MAOC losses in shallow soils than in deeper soils between 50 and 100 cm. The divergent impact of simple C inputs across depths may suggest that MAOC in shallow soils is more susceptible to priming losses, while C inputs into deep soils may instead be more persistent. Collectively, our results suggest that depth gradients in soil C stocks represents a balance between inputs, decomposition, and microbial necromass production and that increases in root C inputs by deep-rooted plants may have the potential to build stable MAOC.

  PublisherDOI

• Comment on egusphere-2026-1482

  2026-04-23

  peer-reviewOpen accessSenior author

  <strong class="journal-contentHeaderColor">Abstract.</strong> Nitrous oxide (N₂O) is a potent greenhouse gas whose emissions are dominated by natural and agricultural soils and are highly heterogeneous and episodic, yet existing observational techniques lack the spatial coverage and near-surface sensitivity needed to resolve this variability. In this study, we evaluate a remote sensing framework that integrates shortwave infrared (SWIR) and thermal infrared (TIR) spectral bands to enhance the detectability of column-integrated N₂O mixing ratio (<em>X</em>_{<em>N</em>₂}<em>O</em>). To implement this, we expand the capacity of the SPLAT&ndash;VLIDORT radiative transfer model to jointly simulate both spectral regions and apply linear sensitivity analysis to quantify the <em>X</em>_{<em>N</em>₂}<em>O</em> measurement error and vertical sensitivity under realistic environmental conditions and instrumental designs. This framework is applied to both airborne and spaceborne instruments to evaluate the influence of platform characteristics on retrieval performance. The joint SWIR&ndash;TIR setting improves near-surface sensitivity relative to the TIR band alone while maintaining the low <em>X</em>_{<em>N</em>₂}<em>O</em> measurement error. It achieves single-sounding measurement error of approximately 3.2 ppb for an airborne instrument with a ground footprint size of 20 m and 1.1 ppb for spaceborne instrument with a footprint size of 0.7 km, while retaining sensitivity to the near-surface layers. Assuming <em>X</em>_{<em>N</em>₂}<em>O</em> variability is observable at twice the precision, natural <em>X</em>_{<em>N</em>₂}<em>O</em> variability inferred from in situ aircraft N₂O observations in the US Midwest becomes observable beyond spatial aggregation scales of &sim; 2.5 km for airborne and &sim; 22 km for spaceborne instruments, subject to significant <em>X</em>_{<em>N</em>₂}<em>O</em> variation between flights. An independent, emission-based detectability analysis indicates that <em>X</em>_{<em>N</em>₂}<em>O</em> variability induced by uniform emissions of 5 nmol m^&minus;2 s^&minus;1 becomes observable beyond spatial averaging of about 2.1 km for airborne and 8.4 km for spaceborne instruments. Together, these results constitute a quantitative basis for N₂O detectability using a joint SWIR&ndash;TIR setting, with a focus on diffuse soil emissions that are more difficult to detect yet dominate the global N₂O budget, and they provide practical guidance for future N₂O dedicated missions.

  PublisherDOI

• Plant-Nitrifier Interactions in Topsoil and Subsoil

  Illinois Data Bank · 2026-01-01

  datasetOpen accessSenior author

  Plants can influence soil microbes through resource acquisition and interference competition, with consequences for ecosystem function such as nitrification. However, how plants alter soil conditions to influence nitrifiers and nitrification rates remains poorly understood, especially in the subsoil. Here, coupling the 15N isotopic pool dilution technique, high throughput sequencing and in situ soil O2 monitoring, we investigated how a deep-rooted perennial grass, miscanthus, versus an adjacent shallow-rooted turfgrass reference shapes nitrifier assembly and function along 1 m soil profiles. In topsoil, the suppression of ammonia (NH3) oxidizing archaea (AOA) and gross nitrification rates in miscanthus relative to the reference likely resulted from nitrifiers being outcompeted by plant roots and heterotrophic bacteria for ammonium (NH4+). The stronger tripartite competition under miscanthus may have been caused in part by the lower soil organic matter (SOM) content, which supported lower gross nitrogen (N) mineralization, the major soil process that produces NH4+. In contrast, below 10 cm soil depth, significantly greater gross nitrification rates were observed in miscanthus compared to the reference. This was likely driven by the significantly lower oxygen (O2) in miscanthus than reference subsoil, which selected against aerobic heterotrophic bacteria but in favor of AOA. Overall, we found that plants can regulate AOA community structure and function through different mechanisms in topsoil and subsoil, with suppression of nitrification in topsoil and enhancement of nitrification in subsoil.

  PublisherDOI

• PaperMind: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs

  arXiv (Cornell University) · 2026-04-23

  preprintOpen access

  Understanding scientific papers requires more than answering isolated questions or summarizing content. It involves an integrated reasoning process that grounds textual and visual information, interprets experimental evidence, synthesizes information across sources, and critically evaluates scientific claims. However, existing benchmarks typically assess these abilities in isolation, making it difficult to evaluate scientific paper understanding as a unified set of interacting cognitive abilities. In this work, we introduce PaperMind, a benchmark designed to evaluate integrated and agent-oriented scientific reasoning over research papers. PaperMind is constructed from real scientific papers across seven domains, including agriculture, biology, chemistry, computer science, medicine, physics, and economics. It comprises four complementary task families that collectively operationalize distinct cognitive facets of scientific paper reasoning, including multimodal grounding, experimental interpretation, cross-source evidence reasoning, and critical assessment. By analyzing model behavior across multiple tasks, PaperMind enables a diagnostic evaluation of integrated scientific reasoning behaviors that are difficult to assess through isolated task evaluations. Extensive experiments on both opensource and closed-source multimodal LLMs reveal consistent performance gaps across tasks, highlighting persistent challenges in integrated scientific reasoning and critique. Our benchmark and dataset are available at https:// github.com/Yanjun-Zhao/PaperMind.

  PublisherDOI

• Towards a Remote Sensing Solution to Quantify Nitrous Oxide Emissions by Integrating Shortwave and Thermal Infrared Bands

  2026-03-26

  articleOpen accessSenior author

  Abstract. Nitrous oxide (N2O) is a potent greenhouse gas whose emissions are dominated by natural and agricultural soils and are highly heterogeneous and episodic, yet existing observational techniques lack the spatial coverage and near-surface sensitivity needed to resolve this variability. In this study, we evaluate a remote sensing framework that integrates shortwave infrared (SWIR) and thermal infrared (TIR) spectral bands to enhance the detectability of column-integrated N2O mixing ratio (XN2O). To implement this, we expand the capacity of the SPLAT–VLIDORT radiative transfer model to jointly simulate both spectral regions and apply linear sensitivity analysis to quantify the XN2O measurement error and vertical sensitivity under realistic environmental conditions and instrumental designs. This framework is applied to both airborne and spaceborne instruments to evaluate the influence of platform characteristics on retrieval performance. The joint SWIR–TIR setting improves near-surface sensitivity relative to the TIR band alone while maintaining the low XN2O measurement error. It achieves single-sounding measurement error of approximately 3.2 ppb for an airborne instrument with a ground footprint size of 20 m and 1.1 ppb for spaceborne instrument with a footprint size of 0.7 km, while retaining sensitivity to the near-surface layers. Assuming XN2O variability is observable at twice the precision, natural XN2O variability inferred from in situ aircraft N2O observations in the US Midwest becomes observable beyond spatial aggregation scales of ∼ 2.5 km for airborne and ∼ 22 km for spaceborne instruments, subject to significant XN2O variation between flights. An independent, emission-based detectability analysis indicates that XN2O variability induced by uniform emissions of 5 nmol m−2 s−1 becomes observable beyond spatial averaging of about 2.1 km for airborne and 8.4 km for spaceborne instruments. Together, these results constitute a quantitative basis for N2O detectability using a joint SWIR–TIR setting, with a focus on diffuse soil emissions that are more difficult to detect yet dominate the global N2O budget, and they provide practical guidance for future N2O dedicated missions.

  PublisherOA PDFDOI

• N ₂ Onet: a global collaborative network facilitating advances in measurement, modeling, and mitigation of agricultural soil nitrous oxide emissions

  Environmental Research Letters · 2026-02-10 · 1 citations

  articleOpen access1st authorCorresponding

  Abstract Nitrogen (N) fertilizer supports global food production, but its use and overuse drive emissions of nitrous oxide (N 2 O), a potent and long-lived greenhouse gas. Understanding the drivers of N 2 O fluxes remains elusive, making it difficult to predict emissions in time and space and to develop and evaluate ways to lower emissions through management. Major scientific uncertainties underlying the understanding of the drivers of N 2 O fluxes identified in a workshop of N 2 O emissions experts include poor process-based understanding of controls on soil N 2 O emissions in the field; insufficient data to reduce uncertainty in N 2 O budgets from the field to regional scales, including N 2 O emission measurements and importantly, field-scale N balances; and high uncertainty in model predictions of soil N 2 O emissions across environmental and management conditions. To reduce these uncertainties, we present the concept of N 2 Onet, a global collaborative initiative to accelerate advances in N 2 O measurement, analyses, and mitigation. N 2 Onet will serve as an observational network of supersites with multi-scale measurements; a database hub for N 2 O flux and ancillary data; and a catalyst for community building, information sharing, and training. By coalescing and coordinating the global community of researchers, N 2 Onet will provide a roadmap for reducing N 2 O emissions from agriculture worldwide.

  PublisherDOI

• PaperMind: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs

  ArXiv.org · 2026-04-23

  articleOpen access

  Understanding scientific papers requires more than answering isolated questions or summarizing content. It involves an integrated reasoning process that grounds textual and visual information, interprets experimental evidence, synthesizes information across sources, and critically evaluates scientific claims. However, existing benchmarks typically assess these abilities in isolation, making it difficult to evaluate scientific paper understanding as a unified set of interacting cognitive abilities. In this work, we introduce PaperMind, a benchmark designed to evaluate integrated and agent-oriented scientific reasoning over research papers. PaperMind is constructed from real scientific papers across seven domains, including agriculture, biology, chemistry, computer science, medicine, physics, and economics. It comprises four complementary task families that collectively operationalize distinct cognitive facets of scientific paper reasoning, including multimodal grounding, experimental interpretation, cross-source evidence reasoning, and critical assessment. By analyzing model behavior across multiple tasks, PaperMind enables a diagnostic evaluation of integrated scientific reasoning behaviors that are difficult to assess through isolated task evaluations. Extensive experiments on both opensource and closed-source multimodal LLMs reveal consistent performance gaps across tasks, highlighting persistent challenges in integrated scientific reasoning and critique. Our benchmark and dataset are available at https:// github.com/Yanjun-Zhao/PaperMind.

  PublisherOA PDF

• The Carbon Budget of Land Conversion: Sugarcane Expansion and Implications for a Sustainable Bioenergy Landscape in <scp>Southeastern United States</scp>

  GCB Bioenergy · 2025-06-26 · 2 citations

  articleOpen access

  ABSTRACT The expansion of sugarcane onto land currently occupied by improved (IMP) and semi‐native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground‐based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3‐year rotation. With 2.3‐ and 5.1‐fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31–0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3‐year rotation (903 ± 335 gC m −2 year −1 ) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 ± 227 gC m −2 year −1 ) but declined in subsequent years to an average 599 ± 90 gC m −2 year −1 —well below those of IMP (933 ± 140 gC m −2 year −1 ) and SN (759 ± 114 gC m −2 year −1 ) pastures—despite a significant 40%–61% increase in soil C inputs. Soil C inputs, however, shifted from root‐dominated in pastures to litter‐dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

  PublisherDOI

• Spatial variability of agricultural soil carbon dioxide and nitrous oxide fluxes: Characterization and recommendations from spatially high-resolution, multi-year dataset

  Agriculture Ecosystems & Environment · 2025-03-27 · 5 citations

  articleOpen access

  Mitigating agricultural soil greenhouse gas (GHG) emissions can contribute to meeting the global climate goals. High spatial and temporal resolution, large-scale, and multi-year data are necessary to characterize and predict spatial patterns of soil GHG fluxes to establish well-informed mitigation strategies, but not many of such datasets are currently available. To address this gap in data we collected two years of in-season soil carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) fluxes at high spatial resolution (7.4 sampling points ha −1 ) from three commercial sites in central Illinois, one conventionally managed continuous corn (2.8 ha in 2021; 5.4 ha in 2022) and two (one site 5.4 ha in 2021 and 2.0 ha in 2022, another site 2.7 ha both years) under conservation practices in corn-soybean rotations. At the field-scale, the spatial variability of CO 2 was comparable across sites, years, and management practices, but N 2 O was on average 77 % more spatially variable in the conventionally managed site. Analysis of N 2 O hotspots revealed that although they represent a similar proportion of the sampling areas across sites (conventional: 12 %; conservation: 13 %), hotspot contribution to field-wide emission was greater in the conventional site than in the conservation sites (conventional: 51 %; conservation: 34 %). Also, the spatial patterns, especially hotspot locations, of both gases were inter-annually inconsistent, with hotspots rarely occurring in the same location. Overall, our result indicated that traditional field-scale monitoring with gas chambers may not be the optimal approach to detect GHG hotspots in row crop systems, due to the unpredictable spatial heterogeneity of management practices. Meanwhile, sensitivity analysis demonstrated that reliable (< 25 % error) field-scale soil GHG flux estimates are attainable when sampled above certain spatial resolutions (1.6 points ha −1 for CO 2 and 5.6 points ha −1 for N 2 O in our dataset). Especially for N 2 O, lower spatial resolutions were prone to underestimating its field-wide flux. • Collected multi-year/site high spatial resolution row-crop soil GHG flux dataset. • N 2 O spatial variability was greater in conventionally managed maize field. • Inter-annually consistent GHG hotspots were rare in our dataset. • GHG hotspots may change due to spatial heterogeneity of management and measurement. • Field-scale GHG flux was reliably estimated at 1.6 (CO 2 ) and 5.6 (N 2 O) points ha −1 .

  PublisherDOI

Recent grants

• Dimensions: Collaborative Research: The Role of Microbial Biodiversity in Controlling Nitrous Oxide Emissions from Soils

  NSF · $691k · 2018–2024

• Unraveling the paradox of dissimilatory nitrate reduction to ammonium in upland soils

  NSF · $158k · 2017–2020

Frequent coauthors

• Evan H. DeLucia

  University of Illinois Urbana-Champaign

  33 shared

• Whendee L. Silver

  University of California, Berkeley

  26 shared

• Angela D. Kent

  University of Illinois Urbana-Champaign

  22 shared

• Emily R. Stuchiner

  21 shared

• William Eddy

  19 shared

• Joanne C. Chee‐Sanford

  Urbana University

  17 shared

• Carl J. Bernacchi

  University of Illinois Urbana-Champaign

  17 shared

• Zhongjie Yu

  16 shared

Labs

• Yang LabPI