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Data and code for: Microalgae-blend tilapia feed eliminates fishmeal and fish oil, improves growth, and is cost viable

Citation

Sarker, Pallab et al. (2020), Data and code for: Microalgae-blend tilapia feed eliminates fishmeal and fish oil, improves growth, and is cost viable, Dryad, Dataset, https://doi.org/10.6071/M3VD5V

Abstract

Aquafeed manufacturers have reduced, but not fully eliminated, fishmeal and fish oil and are seeking cost competitive replacements. We combined two commercially available microalgae, to produce a high-performing fish-free feed for Nile tilapia (Oreochromis niloticus) —the world’s second largest group of farmed fish. We substituted protein-rich defatted biomass of Nannochloropsis oculata (leftover after oil extraction for nutraceuticals) for fishmeal and whole cells of docosahexaenoic acid (DHA)-rich Schizochytrium sp. as substitute for fish oil. Here, we provide the datasets and code that we used to estimate the price of fish-free experimental and reference diets of tilpia in the Scientific Reports manuscript entitled, "Microalgae-blend tilapia feed eliminates fishmeal and fish oil, improves growth, and is cost viable". We include the Rstudio and supporting .csv files for a hedonic analysis of defatted N. oculata meal and whole-cell Schizochytrium sp., non-parametric bootstraps to estimate the median and 95% confidence intervals of commodity and market prices for the formulated tilapia feed ingredients, and for Fig. 2 in the manuscript. 

Methods

Code for bootstrap analysis of commodity and market prices 

We conducted non-parametric bootstraps in Rstudio (v.1.2.5033) based on 10000 replicates using the adjusted bootstrap percentile method to estimate the median and 95% confidence intervals of commodity and market prices for the formulated tilapia feed ingredients that we used in feeditrials from a variety of sources (FAO, 2020; USDA, 2020a; USDA, 2020b; Alibaba, 2019; and Sigma Aldrich vitamin and mineral mixes created in lab; See Supplementary Methods and Tables S5 and S12 for more details). For non-parametric bootstrap code see attached Rstudio file entitled, "bootstrap_confidence_intervals_4_24_2020.R" and supporting .csv files entitled, "Corn_gluten_meal_annual_price.csv", "Fish_meal_annual_price.csv", "Soybean_meal_annual_price.csv", "Wheat_flour_annual_price.csv", "Fish_oil_annual_price.csv", "Lysine_annual_price.csv", "Choline_chloride_annual_price.csv", "DCP_annual_price.csv", "Nanno_meal_annual_price.csv", "Whole_schizo_annual_price.csv"Code for hedonic analysis of defatted N. oculata meal

Code for hedonic analysis of defatted N. oculata meal 

We conducted a hedonic analysis in Rstudio to estimate the price of defatted N. oculata mealThe general methodology of hedonic analysis is described in Maisashvili et al. (2015). We used mixed-effects linear models using maximum likelihood methods (Bates et al., 2015; 2020). We selected crude protein, ether extract, methionine, and lysine as the key input variables in our defatted N. oculata meal model (See Eq. 3 for more details). For the code used in the hedonic analysis, see attached Rstudio file entitled, "Nanno_meal_model_4_20_2020.R" and supporting dataframe .csv entitled, "df_meal_CP_EE_plus_aminos.csv".

Code for hedonic analysis of Schizochytrium sp.

We conducted a hedonic analysis in Rstudio to estimate the price of whole-cell Schizochytrium sp. We selected the top fatty acids (e.g. eicosapentaenoic acid, 20:5n-3; myristic acid, 14:0; palmitoleic acid, 16:1n-7; and palmitic acid, 16:0) present in both the commodity oils (vegetable and fish) and in Schizochytrium sp that did not require an extrapolation (See Eq. 4 for more details). For the code used in the hedonic analysis, see attached Rstudio file entitled, "Schizo_oil_model_4_23_2020.R" and supporting dataframe file entitled, "df_scaled_oil_4_23_2020.csv".

Code for Fig. 2

For the code used to produce Fig. 2, see attached Rstudio file entitled, "Fig_2_4_24_2020.R", and supporting dataframe file entitled,"Feed_price.csv".

References

Alibaba, Product searches, Web accessed Nov. 4, 2019. Available at: https://www.alibaba.com/.

Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).

Bates, D. et al. Linear Mixed-Effects Models using ‘Eigen’ and S4. (2020).

FAO, GIEWS FMPA Tool (v.3.8.0): monitoring and analysis of food prices (Food and Agriculture Organization (FAO) of the United Nations, 2020). Available at:      https://fpma.apps.fao.org/giews/food- prices/tool/public/#/dataset/international.

Maisashvili, A. et al. The values of whole algae and lipid extracted algae meal for aquaculture. Algal Res. 9, 133–142 (2015).

USDA, Agricultural marketing service, Custom reports (United States Department of Agriculture, 2020a). Available at: https://marketnews.usda.gov/mnp/ls-report-config

USDA, Wheat Data, Economic Research Service, (United States Department of Agriculture, 2020b). Available at: https://www.ers.usda.gov/data-products/wheat-data/.

Usage Notes

The attached Rstudio files and supporting dataframes are provided to ensure reproducibility of our study. There are no missing values in the input files. Please see embedded comments in the code provided with the Rstudio files.

Funding

National Institute of Food and Agriculture, Award: 2016-67015-24619

Dartmouth College from the Sherman Fairchild Professorship (to ARK), Dean of the Faculty, and Vranos family gift

University of California, Santa Cruz

National Sea Grant Aquaculture Federal Funding Opportunity, Social, Behavioral and Economic Research Needs in Aquaculture, Award: NOAA-OAR-SG-2019-2005953