About

Stefan Gerber is an Associate Professor in the Department of Soil, Water, and Ecosystem Sciences at the University of Florida, within the Institute of Food and Agricultural Sciences. His research focuses on understanding the cycles of carbon, nitrogen, and other nutrients in land ecosystems through modeling tools. This work aims to elucidate how human activities such as pollution, fertilizer use, land-use changes, and climate change influence biogeochemical cycles and the capacity of terrestrial biospheres to sequester fossil CO2. Gerber's contributions include analyzing long-term watershed records to study nitrogen cycling, developing global dynamic land models to investigate nutrient feedbacks, and exploring the impacts of climate variability on vegetation distribution and carbon storage. His research is critical for addressing environmental challenges related to ecosystem health, climate change, and sustainable land management.

Research topics

• Ecology
• Environmental science
• Biology
• Computer Science
• Neuroscience
• Geology
• Soil science
• Medicine
• Chemistry
• Geography
• Environmental chemistry
• Agronomy
• Forestry
• Animal science

Selected publications

• Climate Shapes the Nitrate Cycling in Riparian Soils Across Biomes

  Global Biogeochemical Cycles · 2026-03-01

  articleOpen access

  Abstract Riparian corridors are key components of the global nitrogen (N) cycle, acting as potential nitrate (NO 3 − ) sinks within landscapes. To better understand the factors driving NO 3 − cycling, we compiled 808 observations of soil moisture, temperature, inorganic N content and net nitrification (NN) rates from 174 riparian sites and used them to build a process‐based model that explored how NN varied as a function of gross nitrification (GN), gross NO 3 − consumption (GC), and their sensitivity to environmental conditions. The empirical data set showed substantial variation in NN across biomes, with rates spanning two orders of magnitude between arctic (median: 0.006 mg N kg −1 d −1 ) and both mediterranean and tropical regions (median: 0.65 mg N kg −1 d −1 ). Globally, soil moisture and temperature together explained 51% of the variation in NN, with soil moisture emerging as the dominant control. Model simulations also revealed that the optimal moisture was lower for GN (WFPS = 60%) than for GC (WFPS = 79%). As a result, saturated soils enhanced NO 3 − buffering potential (i.e., high GC/GN), whereas intermediate moisture levels favored NO 3 − accumulation (i.e., high NN). Finally, GN and GC simulations varied considerably among biomes, reflecting differences in climatic regimes and influencing the potential NO 3 − sink or source behavior of riparian soils. Overall, these findings offer a means to forecast global trends of riparian N cycling and their capacity to mitigate NO 3 − pollution under current and future climatic conditions.

  PublisherDOI

• COGITAO: A Visual Reasoning Framework To Study Compositionality & Generalization

  ArXiv.org · 2025-09-05

  preprintOpen access

  The ability to compose learned concepts and apply them in novel settings is key to human intelligence, but remains a persistent limitation in state-of-the-art machine learning models. To address this issue, we introduce COGITAO, a modular and extensible data generation framework and benchmark designed to systematically study compositionality and generalization in visual domains. Drawing inspiration from ARC-AGI's problem-setting, COGITAO constructs rule-based tasks which apply a set of transformations to objects in grid-like environments. It supports composition, at adjustable depth, over a set of 28 interoperable transformations, along with extensive control over grid parametrization and object properties. This flexibility enables the creation of millions of unique task rules -- surpassing concurrent datasets by several orders of magnitude -- across a wide range of difficulties, while allowing virtually unlimited sample generation per rule. We provide baseline experiments using state-of-the-art vision models, highlighting their consistent failures to generalize to novel combinations of familiar elements, despite strong in-domain performance. COGITAO is fully open-sourced, including all code and datasets, to support continued research in this field.

  PublisherOA PDFDOI

• Hyperspectral signals in the soil: Plant–soil hydraulic connection and disequilibrium as mechanisms of drought tolerance and rapid recovery

  Plant Cell & Environment · 2024-06-26 · 8 citations

  articleOpen access

  Predicting soil water status remotely is appealing due to its low cost and large-scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre-dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought-induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn (Zea mays), which disconnected from the soil during intense drought, and peanut (Arachis hypogaea), which did not. Sweet corn's hydraulic disconnection led to an extended 'hydrated' phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (-5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species-specific considerations in soil water status predictions from canopy reflectance.

  PublisherOA PDFDOI

• Cost-efficient Active Illumination Camera For Hyper-spectral Reconstruction

  arXiv (Cornell University) · 2024-06-27 · 1 citations

  preprintOpen access

  Hyper-spectral imaging has recently gained increasing attention for use in different applications, including agricultural investigation, ground tracking, remote sensing and many other. However, the high cost, large physical size and complicated operation process stop hyperspectral cameras from being employed for various applications and research fields. In this paper, we introduce a cost-efficient, compact and easy to use active illumination camera that may benefit many applications. We developed a fully functional prototype of such camera. With the hope of helping with agricultural research, we tested our camera for plant root imaging. In addition, a U-Net model for spectral reconstruction was trained by using a reference hyperspectral camera's data as ground truth and our camera's data as input. We demonstrated our camera's ability to obtain additional information over a typical RGB camera. In addition, the ability to reconstruct hyperspectral data from multi-spectral input makes our device compatible to models and algorithms developed for hyperspectral applications with no modifications required.

  PublisherOA PDFDOI

• HyperPRI: A dataset of hyperspectral images for underground plant root study

  Computers and Electronics in Agriculture · 2024-08-17 · 6 citations

  article
  PublisherDOI

• HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-09-30 · 1 citations

  preprintOpen access

  Abstract Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut ( Arachis hypogaea ) and sweet corn ( Zea mays ). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatialspectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.

  PublisherOA PDFDOI

• HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

  Harvard Dataverse · 2023-10-12

  datasetOpen access

  <p>Here we present Hyperspectral Plant Root Imagery (HyperPRI), the first available dataset of RGB and HSI data for in situ, non-destructive, underground plant root analysis using machine learning tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. HyperPRI may be applied to semantic segmentation, plant phenotyping, and drought resilience studies. The proposed dataset may also have transferable insights for other datasets containing thin object features among highly textured backgrounds.</p> <b>Dataset Features</b> <ul> <li>Red-green-blue (RGB) and hyperspectral imaging (HSI) data</li> <li>Temporal data for rhizoboxes - plants are monitored from seedling till they are reproductively mature.</li> <li>Thin roots as narrow as 1-3 pixels</li> <li>Highly texture soil background</li> <li>High-resolution spectral data with high correlation between channels</li> </ul> <b>Computer Vision Tasks</b> <ul> <li>Compute root characteristics (length, diameter, angle, count, system architecture, hyperspectral)</li> <li>Determine root turnover</li> <li>Observe drought resiliency and response</li> <li>Compare multiple physical and hyperspectral plant traits across time</li> <li>Investigate texture analysis techniques</li> <li>Segment roots vs. soil</li> </ul>

  PublisherDOI

• Short-term prescribed fire-induced changes in soil microbial communities and nutrients in native rangelands of Florida

  Applied Soil Ecology · 2023 · 16 citations

  Senior authorCorresponding
  • Environmental science
  • Agronomy
  • Ecology
  PublisherDOI

• Effects of increasing complexity in biogeochemistry and hydrology on variability of total phosphorus concentration in models of a low flow subtropical wetland

  Ecological Engineering · 2023-11-06 · 3 citations

  articleCorresponding
  PublisherDOI

• Entscheidungskulturen in der Bismarck-Ära

  2023-04-11

  book

  Unter Rückgriff auf aktuelle Forschungen zur Verflechtung von Kultur- und Politikgeschichte beleuchtet der Band kulturelle Faktoren politischer Entscheidungen im Deutschen Kaiserreich von 1871 bis 1890. Dabei untersucht er das Spannungsverhältnis zwischen dezisionistischen und kompromissorientierten Formen des Entscheidens anhand ausgewählter Bereiche dieses Mehrebenensystems. Konkret nimmt er die Reichsmonarchen, den Bundesrat als Einrichtung föderalen Mitentscheidens, den Reichstag als Ausdruck demokratischer Partizipation, das Militär als extrakonstitutionelles Reservat monarchischer Prärogative sowie die Wirtschaft als konkurrierendes Handlungsfeld in den Blick und berücksichtigt auch den Einsatz von Emotionen in den Entscheidungsprozessen der Akteure.

  PublisherDOI

Frequent coauthors

• Thomas C. Südhof

  45 shared

• Josep Rizo

  The University of Texas Southwestern Medical Center

  26 shared

• Christian Rosenmund

  Charité - Universitätsmedizin Berlin

  22 shared

• Marife Arancillo

  Baylor College of Medicine

  20 shared

• Xinran Liu

  Yale University

  19 shared

• Jong‐Cheol Rah

  18 shared

• Alexander Meyer

  Helios Hospital Berlin-Buch

  17 shared

• Robert E. Hammer

  16 shared

Education

• Doctorate, Climate and Environmental Pysics

  Universität Bern

• Diploma, Earth Science

  Eidgenössische Technische Hochschule Zürich