2026-08-03 バージニア工科大学（Virginia Tech）
Virginia Tech researchers engineered switchgrass, shown in this field, to produce an orange fluorescent protein in its pollen, allowing them to distinguish it from naturally occurring pollen in the environment. Drones equipped with air sampling devices draw airborne particles into a liquid solution as they are flown over the genetically engineered crop. Photo courtesy of David Schmale.
＜関連情報＞
- https://news.vt.edu/articles/2026/07/modeling-pollen-dispersal-david-schmale.html
- https://link.springer.com/article/10.1007/s10661-026-15632-3
畑から空へ：遺伝子組み換えスイッチグラス花粉の大気中拡散の測定とモデリング From field to sky: measurement and modeling of transgenic switchgrass pollen dispersal in the atmosphere
Manu Nimmala,Hope A. Gruszewski,Regina Hanlon,Landon Bilyeu,Tyler Newton,Jessica Stockdale,Reginald J. Millwood,Charles Neal Stewart Jr.,Craig W. Powers,Shane D. Ross,Hosein Foroutan & David G. Schmale III
Environmental Monitoring and Assessment Published:03 August 2026
DOI:https://doi.org/10.1007/s10661-026-15632-3
Abstract
Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, TN, USA. Two hundred transgenic switchgrass plants (Panicum virgatum L. “Performer”) were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a switchgrass ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high- and low-volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
