DOI: 10.6071/m3vd5v
title: Data and code for: Microalgae-blend tilapia feed eliminates fishmeal and fish oil, improves growth, and is cost viable

description: 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. 
description: <em>Code for bootstrap analysis of commodity and market
 prices </em> <em>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</em>
 <em>Code for hedonic analysis of defatted N. oculata
 meal </em> We conducted a hedonic analysis in
 Rstudio to estimate the price of defatted <i>N. oculata</i>
 meal. The 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
 <i>N. oculata</i> 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". <em>Code for hedonic analysis of Schizochytrium sp.</em> We conducted a hedonic analysis in Rstudio to estimate the price of whole-cell <em>Schizochytrium</em> 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 <i>Schizochytrium</i> 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". <em>Code for Fig. 2</em> 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. <i>J. Stat. Softw.</i> <b>67</b>, 1–48 (2015). Bates, D. <i>et al.</i> <i>Linear Mixed-Effects Models using ‘Eigen’ and S4</i>. (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. <i>et al.</i> The values of whole algae and lipid extracted algae meal for aquaculture. <i>Algal Res.</i> <b>9</b>, 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/.
description: 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.