About

Laurie B. Leonelli is associated with the Center for Digital Agriculture at the University of Illinois. The center focuses on research and development in digital and precision agriculture, including AI-driven tools, data collection, storage, transmission, and analysis to optimize various aspects of agriculture such as crop management, water use, and food manufacturing. The center offers interdisciplinary programs, including a Master’s Degree in Engineering with a concentration in Digital Agriculture, and collaborates with international partners like National Taiwan University to host global seminar series on digital and smart agriculture. The center's initiatives include developing decision-support services like CropWizard, which utilizes generative AI to assist agricultural professionals with crop-related inquiries, and supporting projects such as AI AgriBench to build trust in AI agronomy through transparent benchmarking. The center also hosts events like the CDA Conference and AgTech Week, and provides opportunities for undergraduate research through the CDA REU program. Key contributions involve advancing understanding of how data can be used to improve agricultural productivity and sustainability, and exploring innovative AI applications such as CropGPT for farmers. The center's work aims to help researchers, educators, farmers, and industries keep pace with technological transformations in agriculture.

Research topics

• Biology
• Environmental science
• Botany
• Optics
• Computational biology
• Materials science
• Ecology
• Agronomy
• Cell biology
• Physics
• Genetics

Selected publications

• Into the Shadows and Back into Sunlight: Photosynthesis in Fluctuating Light

  Annual Review of Plant Biology · 2022 · 226 citations

  • Environmental science
  • Biology
  • Botany

  assimilation. Transgenic manipulations to accelerate the adjustment in sun-shade transitions have already shown a substantial productivity increase in field trials. Here, we explore means to further accelerate these adjustments and minimize these losses through transgenic manipulation, gene editing, and exploitation of natural variation. Measurement andanalysis of photosynthesis in sun-shade and shade-sun transitions are explained. Factors limiting speeds of adjustment and how they could be modified to effect improved efficiency are reviewed, specifically nonphotochemical quenching (NPQ), Rubisco activation, and stomatal responses.

  DOI

• Soybean photosynthesis and crop yield are improved by accelerating recovery from photoprotection

  Science · 2022 · 335 citations

  • Agronomy
  • Environmental science
  • Biology

  Crop leaves in full sunlight dissipate damaging excess absorbed light energy as heat. This protective dissipation continues after the leaf transitions to shade, reducing crop photosynthesis. A bioengineered acceleration of this adjustment increased photosynthetic efficiency and biomass in tobacco in the field. But could that also translate to increased yield in a food crop? Here we bioengineered the same change into soybean. In replicated field trials, photosynthetic efficiency in fluctuating light was higher and seed yield in five independent transformation events increased by up to 33%. Despite increased seed quantity, seed protein and oil content were unaltered. This validates increasing photosynthetic efficiency as a much needed strategy toward sustainably increasing crop yield in support of future global food security.

  DOI

• Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade

  Nature Communications · 2020 · 168 citations

  • Biology
  • Computational biology
  • Cell biology

  Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq). The transient TF-target interactions captured uncover the early mode-of-action of NIN-LIKE PROTEIN 7 (NLP7), a master regulator of the nitrogen signaling pathway in plants. These transient NLP7 targets captured in root cells using temporal TF perturbation account for 50% of NLP7-regulated genes not detectably bound by NLP7 in planta. Rapid and transient NLP7 binding activates early nitrogen response TFs, which we validate to amplify the NLP7-initiated transcriptional cascade. Our approaches to capture transient TF-target interactions genome-wide can be applied to validate dynamic GRN models for any pathway or organism of interest.

  DOI

Frequent coauthors

• Krishna Niyogi

  49 shared

• Scott A. Coonrod

  Cornell University

  32 shared

• Yanming Wang

  First Affiliated Hospital of Henan University

  31 shared

• C. David Allis

  Rockefeller University

  31 shared

• Julie R. Perlin

  Howard Hughes Medical Institute

  30 shared

• Joanna Wysocka

  Howard Hughes Medical Institute

  29 shared

• Charles H. McDonald

  Baylor College of Medicine

  25 shared

• Richard G. Cook

  University of Tennessee at Chattanooga

  25 shared

Similar researchers at University of Illinois Urbana-Champaign

• Jiawei Hanh-146
• Maria T. Grosse Perdekamph-141
• Gan Zhangh-134
• Jeffrey S. Mooreh-130
• Shuming Nieh-128