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Related Concept Videos

Green Algae01:21

Green Algae

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Green algae, also referred to as chlorophytes, are different from red algae in having the chloroplasts containing chlorophylls a and b, which give them their distinct green hue. However, they lack phycobiliproteins, preventing them from developing the red or blue-green pigmentation seen in red algae. In terms of photosynthetic pigment composition, green algae closely resemble plants and share a close evolutionary relationship with them. Taxonomically Green algae belong to Phylum Chlorophyta in...
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The kingdom Archaeplastida encompasses red and green algae, along with land plants. Unlike other protists with chloroplasts that arose through secondary endosymbiosis, only red and green algae originated from primary endosymbiotic events. This diverse group of eukaryotic organisms contains chlorophyll and performs oxygenic photosynthesis.Algae exist in various forms, from large brown kelp in coastal waters to green scum in puddles and stains on rocks or soil. Some species are responsible for...
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Updated: Jan 9, 2026

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Adaptive Algal Cultivation Enabled by a Monthly Biomass Forecasting System.

Hongxiang Yan1, Song Gao2, Mark S Wigmosta1,3

  • 1Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.

Biotechnology and Bioengineering
|December 6, 2025
PubMed
Summary

Forecasting microalgae biomass production using weather models improves strain and pond depth selection, boosting yields by 15% and enhancing resilience to environmental changes.

Keywords:
Huesemann Algae Biomass Growth ModelPicochlorum celeriTetraselmis striatamonthly biomass forecastingpond water depthstrain selection

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Area of Science:

  • Sustainable biofuels and bioproducts
  • Algal cultivation and biomass production
  • Environmental data assimilation and forecasting

Background:

  • Outdoor microalgae cultivation faces challenges due to environmental variability, impacting biofuel and bioproduct development.
  • Accurate forecasting is needed to optimize operational decisions like strain selection and pond management.
  • Existing methods lack sufficient accuracy for adaptive microalgae farming.

Purpose of the Study:

  • To develop and evaluate an experimental monthly biomass forecasting system for microalgae cultivation.
  • To guide operational decisions, including optimal strain and pond depth selection.
  • To enhance the adaptive capacity and weather resilience of algal production systems.

Main Methods:

  • Utilized the Biomass Assessment Tool (BAT) with climatology-based (NLDAS-2) and multi-model ensemble (NMME) forecasting approaches.
  • Evaluated biomass production strategies for two algal strains (Picochlorum celeri, Tetraselmis striata) across four pond depths (15-30 cm) in Arizona (2020-2024).
  • Compared forecasting model accuracy in predicting optimal strain and pond depth against actual biomass yields.

Main Results:

  • One NMME model achieved 84% accuracy in identifying optimal strain and pond depth monthly; NMME suite accuracies ranged from 74% to 84%.
  • NLDAS-2 climatology-based approach yielded 78% accuracy; strain selection was more accurate (up to 92%) than pond depth selection.
  • Forecast-informed strategies increased average biomass yields by 15% compared to the State-of-Technology, with some months exceeding 40% gains.

Conclusions:

  • Forecast-guided strategies significantly enhance microalgae biomass production and operational efficiency.
  • The developed system offers a scalable and flexible tool for adaptive, weather-resilient algal cultivation.
  • This approach is crucial for advancing sustainable biofuel and bioproduct development under variable environmental conditions.