2022
DOI: 10.31223/x52p8z
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Biophysical potential and uncertainties of global seaweed farming

Abstract: International climate goals require over 5 gigatons/year (Gt/year) of CO2 to be removed from the atmosphere by midcentury. Macroalgae mariculture has been proposed as a strategy for such carbon dioxide removal (CDR). However, the global potential for seaweed cultivation has not been assessed in detail. Here, we develop and use a dynamic seaweed growth model, the Global MacroAlgae Cultivation MODeling System (G-MACMODS), to estimate potential yields of four different types of seaweed worldwide, and test the sen… Show more

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Cited by 5 publications
(9 citation statements)
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References 31 publications
(36 reference statements)
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“…G-MACMODS is a nutrient-constrained, biophysical macroalgal growth model with inputs of temperature, nitrogen, light, flow, wave conditions and amount of seeded biomass 30,53 , that we used to estimate annual seaweed yield per area (either in tons of carbon or tons of dry weight biomass per km 2 per year) 33,34 . In the model, seaweed takes up nitrogen from seawater, and that nitrogen is held in a stored pool before being converted to structural biomass via growth 54 .…”
Section: Biophysical Modelmentioning
confidence: 99%
See 4 more Smart Citations
“…G-MACMODS is a nutrient-constrained, biophysical macroalgal growth model with inputs of temperature, nitrogen, light, flow, wave conditions and amount of seeded biomass 30,53 , that we used to estimate annual seaweed yield per area (either in tons of carbon or tons of dry weight biomass per km 2 per year) 33,34 . In the model, seaweed takes up nitrogen from seawater, and that nitrogen is held in a stored pool before being converted to structural biomass via growth 54 .…”
Section: Biophysical Modelmentioning
confidence: 99%
“…The conversion from stored nitrogen to biomass is based on the minimum internal nitrogen requirements of macroalgae, and the conversion from biomass to units of carbon is based on an average carbon content of macroalgal dry weight (~30%) 55 . The model accounts for farming intensity (sub-grid-scale crowding) and employs a conditional harvest scheme, where harvest is optimized on the basis of growth rate and standing biomass 33 .…”
Section: Biophysical Modelmentioning
confidence: 99%
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