UTILIZATION OF DISTILLERS DRIED GRAINS WITH SOLUBLES IN FISH NUTRITION: 1-REPLACEMENT SOYBEAN MEAL AND YELLOW CORN BY DDGS GRADED LEVELS IN DIET FOR NILE TILAPIA FINGERLINGS ( Oreochromis niloticus).
Samah El-Sharkawy; A. Gabr; F. Khalil · 2013 · Journal of Animal and Poultry Production
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
This study was conducted to evaluate the effect of feeding different levels of DDGS in the diets of tilapia fingerlings on growth performance, feed utilization, chemical composition of the whole fish body, blood hematological and economic efficiency. Therefore, five grading levels of DDGS ( 0, 5, 10, 15 and 20% ( D1- D5)) were used to replace soybean meal and yellow corn protein in approximately five isonitrogenous and isocaloric diets. Fish were stocked in a rearing plastic tank for two weeks adaptation period, then it were stocked at the rate of 5 fish/glass aquarium with initial weight of 6
Abstract by Samah El-Sharkawy; A. Gabr; F. Khalil, Journal of Animal and Poultry Production (2013) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: DOAJ · DOI 10.21608/jappmu.2013.71019
