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|The use of Artemia biomass sampling to predict cyst yields in culture ponds|Baert, P.; Anh, N.T.N.; Burch, A.; Sorgeloos, P. (2002). The use of Artemia biomass sampling to predict cyst yields in culture ponds. Hydrobiologia 477(1-3): 149-153. dx.doi.org/10.1023/A:1021025402584 In: Hydrobiologia. Springer: Berlin. ISSN 0018-8158, more
|Also published as |
- Baert, P.; Anh, N.T.N.; Burch, A.; Sorgeloos, P. (2002). The use of Artemia biomass sampling to predict cyst yields in culture ponds, in: (2002). VLIZ Coll. Rep. 32(2002). VLIZ Collected Reprints: Marine and Coastal Research in Flanders, 32: pp. chapter 2 [Subsequent publication], more
Biomass; Cysts; Sampling; Yield; Artemia franciscana Kellog, 1906 [WoRMS]; Artemia Leach, 1819 [WoRMS]; Marine; Brackish water
Artemia sp.; biomass; sampling; volume; cyst yield
|Authors|| || Top |
- Baert, P.
- Anh, N.T.N.
- Burch, A.
- Sorgeloos, P., more
The possibility of using biomass volume (= mean biomass present in the pond.week-1) to predict the total amount of harvestable cysts (= kg wet weight collected. week-1) produced in a culture pond by an Artemia franciscana population using a mixed model regression was evaluated for two different sampling methods; horizontal transects and vertical point samples. For transects, the following equation was found: `log (0.01 + cyst yields) = -2.05 + 0.025*(biomass volume)' with F(1,4.87) = 8.83 and p = 0.032. For the point samples, the regression was also significant with F(1,55.2) = 13.62 and p = 0.0005 for following equation: `log (0.01 + cyst yield) = -3.613 + 0.021*(biomass volume). As pond effect and interaction terms did not significantly explain a significant portion of the variance for either of the sampling methods (Transects: pond: F(3,14.3) = 2.48; p = 0.103; pond*biomass volume: F(3,3.61) = 4.63; p = 0.0976; Point samples: pond: F(3,44.5) = 0.00; p = 0.999; pond*biomass volume: F(3,44.2) = 0.11; p = 0.954), the variable pond (repeated measurement factor) was not included in the final calculations for the regression equations. Although a combination of factors influences the equation, the high significance levels of the regression indicate biomass volume can be safely used to predict production trends. The low investment requirements of this method make it especially attractive for on farm use, where correctly determining the point of cyst decline will help farmers to allocate resources where needed.